View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and platforms. Finally, we show that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80%. This research has implications for news organizations desiring to increase and to prioritize types of user engagement.

[1]  Andrea Tagarelli,et al.  Online popularity and topical interests through the lens of instagram , 2014, HT.

[2]  Mohsen Kahani,et al.  Mining user interests over active topics on social networks , 2018, Inf. Process. Manag..

[3]  Mohand Boughanem,et al.  Users Are Known by the Company They Keep: Topic Models for Viewpoint Discovery in Social Networks , 2017, CIKM.

[4]  Yongzheng Zhang,et al.  Predicting purchase behaviors from social media , 2013, WWW.

[5]  Krishna P. Gummadi,et al.  Sharing political news: the balancing act of intimacy and socialization in selective exposure , 2014, EPJ Data Science.

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  Chang Wen Chen,et al.  LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity , 2017, CIKM.

[8]  Valeria Noguti,et al.  Post language and user engagement in online content communities , 2016 .

[9]  Ee-Peng Lim,et al.  On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites , 2017, WWW.

[10]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[11]  Balaraman Ravindran,et al.  Will your facebook post be engaging? , 2013, UEO '13.

[12]  Mor Naaman,et al.  Changes in Engagement Before and After Posting to Facebook , 2016, CHI.

[13]  Greg Stoddard,et al.  Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News , 2015, WWW.

[14]  Bernard J. Jansen,et al.  Understanding User-Web Interactions via Web Analytics , 2009, Understanding User-Web Interactions via Web Analytics.

[15]  Murat Can Ganiz,et al.  Semantic text classification: A survey of past and recent advances , 2018, Inf. Process. Manag..

[16]  Michela Del Vicario,et al.  Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum , 2017, ArXiv.

[17]  Gabriella Kazai,et al.  Personalised News and Blog Recommendations based on User Location, Facebook and Twitter User Profiling , 2016, SIGIR.

[18]  Bernard J. Jansen,et al.  Conversing and searching: the causal relationship between social media and web search , 2017, Internet Res..

[19]  Jisun An,et al.  Diversity in Online Advertising: A Case Study of 69 Brands on Social Media , 2018, SocInfo.

[20]  Xia Feng,et al.  Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey , 2017, Multimedia Tools and Applications.

[21]  Jan vom Brocke,et al.  The Impact of Content, Context, and Creator on User Engagement in Social Media Marketing , 2017, HICSS.

[22]  Amy S. Mitchell,et al.  State of the news media 2016 , 2016 .

[23]  Júlio Cesar dos Reis,et al.  Breaking the News: First Impressions Matter on Online News , 2015, ICWSM.

[24]  Mounia Lalmas,et al.  Understanding User Attention and Engagement in Online News Reading , 2016, WSDM.

[25]  Anirban Mahanti,et al.  Characterizing and Predicting Viral-and-Popular Video Content , 2015, CIKM.

[26]  Yu Xu,et al.  Growing Story Forest Online from Massive Breaking News , 2017, CIKM.

[27]  Hae-Chang Rim,et al.  Identifying interesting Twitter contents using topical analysis , 2014, Expert Syst. Appl..

[28]  Bernard J. Jansen,et al.  The Challenges of Creating Engaging Content: Results from a Focus Group Study of a Popular News Media Organization , 2019, CHI Extended Abstracts.

[29]  Daniele Quercia,et al.  Partisan sharing: facebook evidence and societal consequences , 2014, COSN '14.

[30]  Feida Zhu,et al.  It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model , 2013, SDM.

[31]  Jeffrey A. Gottfried,et al.  News use across social media platforms 2016 , 2016 .

[32]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009, J. Assoc. Inf. Sci. Technol..

[33]  Craig MacDonald,et al.  Examining the Coherence of the Top Ranked Tweet Topics , 2016, SIGIR.

[34]  Boon Thau Loo,et al.  Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis , 2017, CIKM.

[35]  Bernardo A. Huberman,et al.  The Pulse of News in Social Media: Forecasting Popularity , 2012, ICWSM.

[36]  Thomas Demeester,et al.  Modeling and predicting the popularity of online news based on temporal and content-related features , 2017, Multimedia Tools and Applications.

[37]  Hongfei Yan,et al.  Comparing Twitter and Traditional Media Using Topic Models , 2011, ECIR.

[38]  Tim Weninger,et al.  Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources , 2018, ACL.

[39]  Ravi Kant,et al.  A Large Scale Prediction Engine for App Install Clicks and Conversions , 2017, CIKM.

[40]  Mounia Lalmas,et al.  Models of user engagement , 2012, UMAP.

[41]  Cen Chen,et al.  Aspect-Based Helpfulness Prediction for Online Product Reviews , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).

[42]  Ee-Peng Lim,et al.  Finding Bursty Topics from Microblogs , 2012, ACL.

[43]  Germana Scepi,et al.  Combining different evaluation systems on social media for measuring user satisfaction , 2018, Inf. Process. Manag..

[44]  Hsi-Peng Lu,et al.  Why people use social networking sites: An empirical study integrating network externalities and motivation theory , 2011, Comput. Hum. Behav..

[45]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.