Social mining for terroristic behavior detection through Arabic tweets characterization

In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Alhalabi, Wadee [verfasserIn] Jussila, Jari [verfasserIn] Jambi, Kamal [verfasserIn] Visvizi, Anna [verfasserIn] Qureshi, Hafsa [verfasserIn] Lytras, Miltiadis [verfasserIn] Malibari, Areej [verfasserIn] Adham, Raniah Samir [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis

Übergeordnetes Werk:	Enthalten in: Future generation computer systems - Amsterdam [u.a.] : Elsevier Science, 1984, 116, Seite 132-144
Übergeordnetes Werk:	volume:116 ; pages:132-144

DOI / URN:	10.1016/j.future.2020.10.027

Katalog-ID:	ELV005193125

Internformat


LEADER	01000caa a22002652 4500
001	ELV005193125
003	DE-627
005	20230524160024.0
007	cr uuu---uuuuu
008	230503s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.future.2020.10.027 \|2 doi
035			\|a (DE-627)ELV005193125
035			\|a (ELSEVIER)S0167-739X(20)33004-1
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|q DE-600
084			\|a 54.00 \|2 bkl
100	1		\|a Alhalabi, Wadee \|e verfasserin \|4 aut
245	1	0	\|a Social mining for terroristic behavior detection through Arabic tweets characterization
264		1	\|c 2020
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
650		4	\|a Sentiment antiterrorism detection
650		4	\|a Social mining
650		4	\|a Twitter
650		4	\|a Arabic tweets
650		4	\|a Algorithms
650		4	\|a Sentiment analysis
700	1		\|a Jussila, Jari \|e verfasserin \|4 aut
700	1		\|a Jambi, Kamal \|e verfasserin \|4 aut
700	1		\|a Visvizi, Anna \|e verfasserin \|4 aut
700	1		\|a Qureshi, Hafsa \|e verfasserin \|4 aut
700	1		\|a Lytras, Miltiadis \|e verfasserin \|4 aut
700	1		\|a Malibari, Areej \|e verfasserin \|4 aut
700	1		\|a Adham, Raniah Samir \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Future generation computer systems \|d Amsterdam [u.a.] : Elsevier Science, 1984 \|g 116, Seite 132-144 \|h Online-Ressource \|w (DE-627)320604284 \|w (DE-600)2020551-X \|w (DE-576)094399212 \|x 0167-739X \|7 nnns
773	1	8	\|g volume:116 \|g pages:132-144
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.00 \|j Informatik: Allgemeines
951			\|a AR
952			\|d 116 \|h 132-144

Indexfelder

author_variant	w a wa j j jj k j kj a v av h q hq m l ml a m am r s a rs rsa
matchkey_str	article:0167739X:2020----::oiliigotroitceaireetotruhrbc
hierarchy_sort_str	2020
bklnumber	54.00
publishDate	2020
allfields	10.1016/j.future.2020.10.027 doi (DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Alhalabi, Wadee verfasserin aut Social mining for terroristic behavior detection through Arabic tweets characterization 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis Jussila, Jari verfasserin aut Jambi, Kamal verfasserin aut Visvizi, Anna verfasserin aut Qureshi, Hafsa verfasserin aut Lytras, Miltiadis verfasserin aut Malibari, Areej verfasserin aut Adham, Raniah Samir verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 116, Seite 132-144 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:116 pages:132-144 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 116 132-144
spelling	10.1016/j.future.2020.10.027 doi (DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Alhalabi, Wadee verfasserin aut Social mining for terroristic behavior detection through Arabic tweets characterization 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis Jussila, Jari verfasserin aut Jambi, Kamal verfasserin aut Visvizi, Anna verfasserin aut Qureshi, Hafsa verfasserin aut Lytras, Miltiadis verfasserin aut Malibari, Areej verfasserin aut Adham, Raniah Samir verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 116, Seite 132-144 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:116 pages:132-144 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 116 132-144
allfields_unstemmed	10.1016/j.future.2020.10.027 doi (DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Alhalabi, Wadee verfasserin aut Social mining for terroristic behavior detection through Arabic tweets characterization 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis Jussila, Jari verfasserin aut Jambi, Kamal verfasserin aut Visvizi, Anna verfasserin aut Qureshi, Hafsa verfasserin aut Lytras, Miltiadis verfasserin aut Malibari, Areej verfasserin aut Adham, Raniah Samir verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 116, Seite 132-144 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:116 pages:132-144 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 116 132-144
allfieldsGer	10.1016/j.future.2020.10.027 doi (DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Alhalabi, Wadee verfasserin aut Social mining for terroristic behavior detection through Arabic tweets characterization 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis Jussila, Jari verfasserin aut Jambi, Kamal verfasserin aut Visvizi, Anna verfasserin aut Qureshi, Hafsa verfasserin aut Lytras, Miltiadis verfasserin aut Malibari, Areej verfasserin aut Adham, Raniah Samir verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 116, Seite 132-144 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:116 pages:132-144 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 116 132-144
allfieldsSound	10.1016/j.future.2020.10.027 doi (DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Alhalabi, Wadee verfasserin aut Social mining for terroristic behavior detection through Arabic tweets characterization 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis. Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis Jussila, Jari verfasserin aut Jambi, Kamal verfasserin aut Visvizi, Anna verfasserin aut Qureshi, Hafsa verfasserin aut Lytras, Miltiadis verfasserin aut Malibari, Areej verfasserin aut Adham, Raniah Samir verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 116, Seite 132-144 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:116 pages:132-144 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 116 132-144
language	English
source	Enthalten in Future generation computer systems 116, Seite 132-144 volume:116 pages:132-144
sourceStr	Enthalten in Future generation computer systems 116, Seite 132-144 volume:116 pages:132-144
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis
dewey-raw	004
isfreeaccess_bool	false
container_title	Future generation computer systems
authorswithroles_txt_mv	Alhalabi, Wadee @@aut@@ Jussila, Jari @@aut@@ Jambi, Kamal @@aut@@ Visvizi, Anna @@aut@@ Qureshi, Hafsa @@aut@@ Lytras, Miltiadis @@aut@@ Malibari, Areej @@aut@@ Adham, Raniah Samir @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320604284
dewey-sort	14
id	ELV005193125
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005193125</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524160024.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.future.2020.10.027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005193125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-739X(20)33004-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Alhalabi, Wadee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Social mining for terroristic behavior detection through Arabic tweets characterization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment antiterrorism detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Twitter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Arabic tweets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jussila, Jari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jambi, Kamal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Visvizi, Anna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qureshi, Hafsa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lytras, Miltiadis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malibari, Areej</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Adham, Raniah Samir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Future generation computer systems</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1984</subfield><subfield code="g">116, Seite 132-144</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320604284</subfield><subfield code="w">(DE-600)2020551-X</subfield><subfield code="w">(DE-576)094399212</subfield><subfield code="x">0167-739X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:116</subfield><subfield code="g">pages:132-144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">116</subfield><subfield code="h">132-144</subfield></datafield></record></collection>
author	Alhalabi, Wadee
spellingShingle	Alhalabi, Wadee ddc 004 bkl 54.00 misc Sentiment antiterrorism detection misc Social mining misc Twitter misc Arabic tweets misc Algorithms misc Sentiment analysis Social mining for terroristic behavior detection through Arabic tweets characterization
authorStr	Alhalabi, Wadee
ppnlink_with_tag_str_mv	@@773@@(DE-627)320604284
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	0167-739X
topic_title	004 DE-600 54.00 bkl Social mining for terroristic behavior detection through Arabic tweets characterization Sentiment antiterrorism detection Social mining Twitter Arabic tweets Algorithms Sentiment analysis
topic	ddc 004 bkl 54.00 misc Sentiment antiterrorism detection misc Social mining misc Twitter misc Arabic tweets misc Algorithms misc Sentiment analysis
topic_unstemmed	ddc 004 bkl 54.00 misc Sentiment antiterrorism detection misc Social mining misc Twitter misc Arabic tweets misc Algorithms misc Sentiment analysis
topic_browse	ddc 004 bkl 54.00 misc Sentiment antiterrorism detection misc Social mining misc Twitter misc Arabic tweets misc Algorithms misc Sentiment analysis
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Future generation computer systems
hierarchy_parent_id	320604284
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Future generation computer systems
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)320604284 (DE-600)2020551-X (DE-576)094399212
title	Social mining for terroristic behavior detection through Arabic tweets characterization
ctrlnum	(DE-627)ELV005193125 (ELSEVIER)S0167-739X(20)33004-1
title_full	Social mining for terroristic behavior detection through Arabic tweets characterization
author_sort	Alhalabi, Wadee
journal	Future generation computer systems
journalStr	Future generation computer systems
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2020
contenttype_str_mv	zzz
container_start_page	132
author_browse	Alhalabi, Wadee Jussila, Jari Jambi, Kamal Visvizi, Anna Qureshi, Hafsa Lytras, Miltiadis Malibari, Areej Adham, Raniah Samir
container_volume	116
class	004 DE-600 54.00 bkl
format_se	Elektronische Aufsätze
author-letter	Alhalabi, Wadee
doi_str_mv	10.1016/j.future.2020.10.027
dewey-full	004
author2-role	verfasserin
title_sort	social mining for terroristic behavior detection through arabic tweets characterization
title_auth	Social mining for terroristic behavior detection through Arabic tweets characterization
abstract	In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
abstractGer	In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
abstract_unstemmed	In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Social mining for terroristic behavior detection through Arabic tweets characterization
remote_bool	true
author2	Jussila, Jari Jambi, Kamal Visvizi, Anna Qureshi, Hafsa Lytras, Miltiadis Malibari, Areej Adham, Raniah Samir
author2Str	Jussila, Jari Jambi, Kamal Visvizi, Anna Qureshi, Hafsa Lytras, Miltiadis Malibari, Areej Adham, Raniah Samir
ppnlink	320604284
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.future.2020.10.027
up_date	2024-07-06T17:07:52.803Z
_version_	1803850269648224256
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005193125</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524160024.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.future.2020.10.027</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005193125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-739X(20)33004-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Alhalabi, Wadee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Social mining for terroristic behavior detection through Arabic tweets characterization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment antiterrorism detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Twitter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Arabic tweets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jussila, Jari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jambi, Kamal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Visvizi, Anna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qureshi, Hafsa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lytras, Miltiadis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malibari, Areej</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Adham, Raniah Samir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Future generation computer systems</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1984</subfield><subfield code="g">116, Seite 132-144</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320604284</subfield><subfield code="w">(DE-600)2020551-X</subfield><subfield code="w">(DE-576)094399212</subfield><subfield code="x">0167-739X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:116</subfield><subfield code="g">pages:132-144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">116</subfield><subfield code="h">132-144</subfield></datafield></record></collection>
score	7.3993654

Nicht das Richtige dabei?

Schreiben Sie uns!

Social mining for terroristic behavior detection through Arabic tweets characterization

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?