Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

By Bongwon Suh, Lichan Hong, Peter Pirolli, Ed H. Chi. 2010 IEEE Second International Conference on Social Computing, 2010

1,299 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ddb3324ec2afeabea6755199aef1af2788b4b1a9

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Citations in the corpus

Quantifying the invisible audience in social networks (2013, background): “While previous work has focused on highly visible audience signals such as retweets [5, 31], this work allows us to examine the invisible undercurrents of attention in social media use.”
Predicting Responses to Microblog Posts (2012, background): “The task of predicting responses in social networks has been investigated previously: Hong et al. (2011) focused on predicting responses for highly popular items, Rowe et al. (2011) targeted the prediction of conversations and their length and Suh et al. (2010) predicted retweets.”
Twitter as Data (2018, background): “The most common way links are analyzed is for researchers to note the percentage of tweets containing them (Suh et al., 2010; Steinert-Threlkeld, 2016), but I am aware of no articles that analyze the content of those links.”
#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, background): “Not only because group members might have focused their energy on making original statements, but this type of commentary and conversational tweets are naturally less appealing for retweeting [45].”
On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, background): “Among the remaining papers, boyd et al. (2010) base their conclusions on qualitative research, and Suh et al. (2010) concur by citing that paper.”
The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter (2014, result): “The appearance of [hashtag] in the best TAC+ff+time unigrams is consistent with prior research in non-TAC settings (Suh et al., 2010; Petrović et al., 2011).”
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior (2012, background): “, [33]).”
Faces engage us: photos with faces attract more likes and comments on Instagram (2014, background): “found that the presence of URLs and hashtags in tweets predicted more retweeting, as did a richer connection with the community [44].”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, background): “Online social networking services, such as Facebook, Youtube, and Twitter, allow their users to post and share content in the form of posts, images, and videos [9, 17, 21, 30].”
What's in a @name? How Name Value Biases Judgment of Microblog Authors (2011, background): “Recent research also explain that user’s perception about retweeeting could be biased based on several characteristi cs like url, hashtags, etc (Suh et al. 2010).”
Predicting popular messages in Twitter (2011, background): “[2] studied a variety of factors that might influence retweets without explicitly showing their effectiveness in a classification framework.”
Detecting and Tracking Political Abuse in Social Media (2011, background): “…analyzed to study consumer reactions to specific brands (Jansen et al. 2009), the use of tags to alter content (Huang, Thornton, and Efthimiadis 2010), its relation to headline news (Kwak et al. 2010), and the factors that influence the probability of a meme to be retweeted (Suh et al. 2010).”
Virality Prediction and Community Structure in Social Networks (2013, background): “The questions of how memes spread and which will go viral have recently attracted much attention across disciplines, including marketing [6, 19], network science [20, 21], communication [22], and social media analytics [23–25].”
Predictive Analysis on Twitter: Techniques and Applications (2018, background): “Researchers found URLs in a tweet to be discriminative in various studies such as sentiment analysis [21, 22], popularity prediction [23, 24], spam detection [25].”
Bad news travel fast: a content-based analysis of interestingness on Twitter (2011, methodology): “[14] state that contextual features include basic aspects of the graph structure, e.”