Did clickbait crack the code on virality?
Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mukherjee, Prithwiraj [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Academy of Marketing Science 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Academy of Marketing Science - Springer US, 1973, 50(2022), 3 vom: 29. Jan., Seite 482-502 |
|---|---|
| Übergeordnetes Werk: |
volume:50 ; year:2022 ; number:3 ; day:29 ; month:01 ; pages:482-502 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11747-021-00830-x |
|---|
| Katalog-ID: |
OLC2078427535 |
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| 520 | |a Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as a manipulative attempt, leading to source derogation where the publisher may be perceived as less competent and trustworthy. This, in turn, may reduce some readers’ intention to share content. Using a controlled experiment, we confirm that high-emotional headlines are shared more and show evidence that clickbait often leads to inferences of manipulative intent and source derogation. We then use a well-known secondary data set containing 19,386 articles from 27 leading online publishers. We supplement it with Twitter share data, sentiment analysis, topic modeling, and additional control variables. We confirm that, on average, clickbait articles elicit far fewer shares than non-clickbait articles. Our results are stable, with large effect sizes even after controlling for endogenous selection. | ||
| 650 | 4 | |a Social media | |
| 650 | 4 | |a Clickbait | |
| 650 | 4 | |a Persuasion Knowledge Model | |
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| 650 | 4 | |a Sentiment analysis | |
| 650 | 4 | |a Propensity score matching | |
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| 700 | 1 | |a De Bruyn, Arnaud |4 aut | |
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10.1007/s11747-021-00830-x doi (DE-627)OLC2078427535 (DE-He213)s11747-021-00830-x-p DE-627 ger DE-627 rakwb eng 330 VZ 3,2 ssgn Mukherjee, Prithwiraj verfasserin (orcid)0000-0003-2498-5599 aut Did clickbait crack the code on virality? 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Academy of Marketing Science 2021 Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as a manipulative attempt, leading to source derogation where the publisher may be perceived as less competent and trustworthy. This, in turn, may reduce some readers’ intention to share content. Using a controlled experiment, we confirm that high-emotional headlines are shared more and show evidence that clickbait often leads to inferences of manipulative intent and source derogation. We then use a well-known secondary data set containing 19,386 articles from 27 leading online publishers. We supplement it with Twitter share data, sentiment analysis, topic modeling, and additional control variables. We confirm that, on average, clickbait articles elicit far fewer shares than non-clickbait articles. Our results are stable, with large effect sizes even after controlling for endogenous selection. Social media Clickbait Persuasion Knowledge Model Source derogation Sharing Topic modeling Sentiment analysis Propensity score matching Dutta, Souvik aut De Bruyn, Arnaud aut Enthalten in Journal of the Academy of Marketing Science Springer US, 1973 50(2022), 3 vom: 29. Jan., Seite 482-502 (DE-627)182223736 (DE-600)1187865-4 (DE-576)040097765 0092-0703 nnns volume:50 year:2022 number:3 day:29 month:01 pages:482-502 https://doi.org/10.1007/s11747-021-00830-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 AR 50 2022 3 29 01 482-502 |
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Did clickbait crack the code on virality? |
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Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as a manipulative attempt, leading to source derogation where the publisher may be perceived as less competent and trustworthy. This, in turn, may reduce some readers’ intention to share content. Using a controlled experiment, we confirm that high-emotional headlines are shared more and show evidence that clickbait often leads to inferences of manipulative intent and source derogation. We then use a well-known secondary data set containing 19,386 articles from 27 leading online publishers. We supplement it with Twitter share data, sentiment analysis, topic modeling, and additional control variables. We confirm that, on average, clickbait articles elicit far fewer shares than non-clickbait articles. Our results are stable, with large effect sizes even after controlling for endogenous selection. © Academy of Marketing Science 2021 |
| abstractGer |
Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as a manipulative attempt, leading to source derogation where the publisher may be perceived as less competent and trustworthy. This, in turn, may reduce some readers’ intention to share content. Using a controlled experiment, we confirm that high-emotional headlines are shared more and show evidence that clickbait often leads to inferences of manipulative intent and source derogation. We then use a well-known secondary data set containing 19,386 articles from 27 leading online publishers. We supplement it with Twitter share data, sentiment analysis, topic modeling, and additional control variables. We confirm that, on average, clickbait articles elicit far fewer shares than non-clickbait articles. Our results are stable, with large effect sizes even after controlling for endogenous selection. © Academy of Marketing Science 2021 |
| abstract_unstemmed |
Abstract Although clickbait is a ubiquitous tactic in digital media, we challenge the popular belief that clickbait systematically leads to enhanced sharing of online content on social media. Using the Persuasion Knowledge Model, we predict that clickbait tactics may be perceived by some readers as a manipulative attempt, leading to source derogation where the publisher may be perceived as less competent and trustworthy. This, in turn, may reduce some readers’ intention to share content. Using a controlled experiment, we confirm that high-emotional headlines are shared more and show evidence that clickbait often leads to inferences of manipulative intent and source derogation. We then use a well-known secondary data set containing 19,386 articles from 27 leading online publishers. We supplement it with Twitter share data, sentiment analysis, topic modeling, and additional control variables. We confirm that, on average, clickbait articles elicit far fewer shares than non-clickbait articles. Our results are stable, with large effect sizes even after controlling for endogenous selection. © Academy of Marketing Science 2021 |
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| title_short |
Did clickbait crack the code on virality? |
| url |
https://doi.org/10.1007/s11747-021-00830-x |
| remote_bool |
false |
| author2 |
Dutta, Souvik De Bruyn, Arnaud |
| author2Str |
Dutta, Souvik De Bruyn, Arnaud |
| ppnlink |
182223736 |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s11747-021-00830-x |
| up_date |
2024-07-03T20:23:17.554Z |
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1803590773040480256 |
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7.4008894 |