Composing tweets to increase retweets

Jalali, Nima; Papatla, Purushottam

doi:10.1016/j.ijresmar.2019.05.001

Cited by 27 publications

(15 citation statements)

References 33 publications

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“…Zhang et al ( 2017 ) establish that message-user fit heavily influences sharing. Jalali and Papatla ( 2019 ) find that tweets that start with more topic-related words get retweeted more. Berger ( 2014 ) provides a comprehensive review of sharing motivations, noting that motivations could range from (a) impression management where individuals convey positive impressions of themselves, (b) emotion regulation where individuals manage their emotions, (c) information acquisition where individuals seek inputs from others, (d) social bonding where individuals seek to connect with others, and (e) persuading others.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Did clickbait crack the code on virality?

Mukherjee

Souvik

Bruyn

2022

J. of the Acad. Mark. Sci.

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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. Supplementary Information The online version contains supplementary material available at 10.1007/s11747-021-00830-x.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Did clickbait crack the code on virality?

Mukherjee

Souvik

Bruyn

2022

J. of the Acad. Mark. Sci.

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“…Furthermore, another line of inquiry is concerned with what makes some tweets more likely to be retweeted than others. According to Jalali & Papatla (2019), the propensity of retweeting might be influenced by the position or visibility the tweets have, and by the number of followers Twitter users have. The most influential Twitter users have greater probabilities of gaining a higher number of retweets than others.…”

Section: Predictive Models Using Twitter Data: Retweet Analysismentioning

confidence: 99%

“…The most influential Twitter users have greater probabilities of gaining a higher number of retweets than others. Jalali and Papatla (2019) also include posting time and sharing similar viewpoints in tweets as influential features for propagating tweets. And, Shi et al (2018) state that the presence of URLs and hashtags increases the chance of a tweet to be retweeted.…”

Section: Predictive Models Using Twitter Data: Retweet Analysismentioning

confidence: 99%

Predicting tweet impact using a novel evidential reasoning prediction method

Rivadeneira

Yang

López-Ibáñez

2021

Expert Systems with Applications

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“…The terms and conditions of Twitter API do not allow fetching user profiles from TweetIDs. The proposed work is an attempt to provide an alternative way to handle this problem by using public Twitter archives [ 24 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

“…The use of limited features available in Twitter API can be one of the solutions to generate domain independent, language independent and general purpose analysis on very large datasets. Recent studies [ 24 , 48 ] have found that due to concerns of user privacy and restrictions imposed by social media companies on the distribution and sharing of dataset makes it very difficult to reproduce the same dataset for social media analysis [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

Role of twitter user profile features in retweet prediction for big data streams

Sharma

Gupta

2022

Multimed Tools Appl

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To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely “Author total activity”, “Author total activity per year”, and “Author tweets per year”. The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

show abstract

Composing tweets to increase retweets

Cited by 27 publications

References 33 publications

Did clickbait crack the code on virality?

Did clickbait crack the code on virality?

Predicting tweet impact using a novel evidential reasoning prediction method

Role of twitter user profile features in retweet prediction for big data streams

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