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, … Show more
“…This also provides the validity of dark behavior of social media users based on large-scale data. It also contrasts with the recent findings of Mukherjee et al. (2022) who suggest that click-baits will not lead to higher sharing.…”
Section: Discussioncontrasting
confidence: 99%
“…Studies on the psychological process involved in click-bait response of the users are very limited. While some researchers have focused on the emotional response of the click-bait headline (Pengnate, 2019), others have focused on how the impact of click-baitiness on source derogation and sharing behavior of the users (Mukherjee et al, 2022). However, none of these studies focused on the drivers of clickbaitiness and the psychological process under it.…”
Section: Click-baitiness and Click-bait Virality 2485mentioning
confidence: 99%
“…Click-baits attract a lot of traffic as they create inquisitiveness in the mind of the readers. However, very few readers are expected to share the click-bait on social media on their own as sharing click-bait may impact their status on the social networks negatively (Mukherjee et al, 2022). Click-baits are mostly shared by the ad spend by the marketers.…”
Section: Click-baitiness and Click-bait Virality 2485mentioning
confidence: 99%
“…However, no exploration has been done on how a clickbait can become viral and what are the antecedents of such an event. Although, Mukherjee et al (2022) explored the linkage between click-baitiness and sharing of social media posts, they found that click-baits are less likely to become viral on their own as the users may understand the manipulative intent of the click-baits and will derogate the source. Therefore, their findings suggest that sharing of dark content will not happen in the context IMDS 122,11 of click-bait, which is in contrast to past literature.…”
PurposeIn the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of a click-bait.Design/methodology/approachThis study has used 17,745 tweets from Twitter with 4,370 click-baits from top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality.FindingsThis study finds that language formality, readability, sentiment scores and proper noun usage of social media posts and various parts of the target article plays differential and important roles in click-baitiness and click-bait virality.Research limitations/implicationsThe paper contributes toward the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper also provides explanability to the econometric and machine learning predictive models, thus performing methodological contribution too.Practical implicationsThe paper helps social media managers create a mechanism to detect click-baits and also predict which ones of them can become viral so that corrective measures can be taken.Originality/valueTo the best of the authors’ knowledge, this is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality.
“…This also provides the validity of dark behavior of social media users based on large-scale data. It also contrasts with the recent findings of Mukherjee et al. (2022) who suggest that click-baits will not lead to higher sharing.…”
Section: Discussioncontrasting
confidence: 99%
“…Studies on the psychological process involved in click-bait response of the users are very limited. While some researchers have focused on the emotional response of the click-bait headline (Pengnate, 2019), others have focused on how the impact of click-baitiness on source derogation and sharing behavior of the users (Mukherjee et al, 2022). However, none of these studies focused on the drivers of clickbaitiness and the psychological process under it.…”
Section: Click-baitiness and Click-bait Virality 2485mentioning
confidence: 99%
“…Click-baits attract a lot of traffic as they create inquisitiveness in the mind of the readers. However, very few readers are expected to share the click-bait on social media on their own as sharing click-bait may impact their status on the social networks negatively (Mukherjee et al, 2022). Click-baits are mostly shared by the ad spend by the marketers.…”
Section: Click-baitiness and Click-bait Virality 2485mentioning
confidence: 99%
“…However, no exploration has been done on how a clickbait can become viral and what are the antecedents of such an event. Although, Mukherjee et al (2022) explored the linkage between click-baitiness and sharing of social media posts, they found that click-baits are less likely to become viral on their own as the users may understand the manipulative intent of the click-baits and will derogate the source. Therefore, their findings suggest that sharing of dark content will not happen in the context IMDS 122,11 of click-bait, which is in contrast to past literature.…”
PurposeIn the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of a click-bait.Design/methodology/approachThis study has used 17,745 tweets from Twitter with 4,370 click-baits from top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality.FindingsThis study finds that language formality, readability, sentiment scores and proper noun usage of social media posts and various parts of the target article plays differential and important roles in click-baitiness and click-bait virality.Research limitations/implicationsThe paper contributes toward the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper also provides explanability to the econometric and machine learning predictive models, thus performing methodological contribution too.Practical implicationsThe paper helps social media managers create a mechanism to detect click-baits and also predict which ones of them can become viral so that corrective measures can be taken.Originality/valueTo the best of the authors’ knowledge, this is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality.
“…The usage of social applications is slowly transforming how people think and act. As of 2019, 69% of UK teenagers (age [12][13][14][15][16][17] are on at least one social platform [2]. The aim of this research is to find out if the online environment of Tik Tok contributes to the low interactivity of UK audience and propose strategies to alleviate the negative effects.…”
Section: Introduction 11 Research Backgroundmentioning
With the rising concern of Tik Tok videos propagating misinformation to younger viewers, research on the adversary effects of the platform of Tik Tok has been few and far between. This research applies a simplistic approach of gathering and observing numbers related to 60 content creators on YouTube and Tik Tok (30 on each platform) for the comparative analysis. The analysis is largely built upon the average video length of each creator’s most recent 50 videos and the frequency of numbers of lines of each top comment. The correlation between the two is found relatively weak within one platform. However, videos on YouTube have significant lengthier top comments than those on Tik Tok most of the time, which suggests a higher level of thinking and commitment on the former in general. The two factors along with different features on the two platforms that are tailored towards different types of audiences contribute to the levels of discussion and the ways users treat information and misinformation. Implications and recommendations in regard to Tik Tok’s environment are further discussed.
To preview digital content and arouse consumers' interest, online providers often use short teasers designed in an unfinished form, such that the teaser begins a new sentence but does not finish it. These teasers aim to create curiosity and trigger consumption of the advertised content. However, we reveal that consumers' reactions to unfinished teasers are not always positive. The results from a qualitative pilot study and five experimental studies show that consumers react negatively to unfinished teasers for paid content, as demonstrated by reduced purchases. This effect reverses for free content, in that unfinished teasers lead to more consumption. We explain this reversal by showing that the barrier associated with paid content (i.e., the payment requirement) activates consumers' persuasion knowledge and suppresses any positive curiosity‐induced effects, which does not occur when content is available for free. These findings offer novel insights into the complexity of consumers' reactions to prevalent advertising techniques designed to promote content consumption in digital marketplaces.
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