ABSTRACT
Physical devices, such as smartphones and laptops, provide the key interface through which users engage with the social media world. Yet despite the broad range of devices used on social media platforms, relatively little is known about how usage varies on a device to device level. In this work, we use a 10 sample of Twitter data spanning two consecutive years and encompassing 365.98 million users to perform a longitudinal, measurement-driven analysis of five prevalent device types - Android, iPhone-iOS, BlackBerry, other mobile devices, and nonmobile devices - across the time period. We study the global and regional usage patterns, as well as the regional distribution, of devices; investigate differences and similarities in behavioral patterns across devices with respect to tweet sentiment, daytime usage patterns, and feature usage (such as mentions, URLs, hashtags) over time; and quantify the level of "device homophily" (i.e., assortativity) within the Twitter device network. Our results reveal that key variations exist among these device groups, in addition to notable similarities. To the best of our knowledge, this is the first large-scale longitudinal analysis of various distinct Twitter devices.
- Jisun An and Ingmar Weber. 2016. #greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter. CoRR abs/1603.01973 (2016). http://arxiv.org/abs/1603.01973Google Scholar
- Ryan Compton, David Jurgens, and David Allen. 2014. Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization. CoRR abs/1404.7152 (2014). http://arxiv.org/abs/1404.7152.Google Scholar
- Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities. In Proceedings of the 22Nd International Conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 307--318. Google ScholarDigital Library
- Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, and Christopher M. Danforth. 2011. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. CoRR abs/1101.5120 (2011). http://dblp.uni-trier.de/db/journals/corr/corr1101.html# abs-1101--5120Google Scholar
- David Easley and Jon Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, USA. Google ScholarDigital Library
- Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in Smartphone Usage. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10). New York, NY, USA, 179--194. Google ScholarDigital Library
- Flavio Figueiredo, Fabrício Benevenuto, and Jussara M. Almeida. 2011. The Tube over Time: Characterizing Popularity Growth of Youtube Videos. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM '11). New York, NY, USA, 745--754. Google ScholarDigital Library
- Jeon Hyung Kang and Kristina Lerman. 2012. Using Lists to Measure Homophily on Twitter. In AAAI Technical Report WS-12-09. Association for the Advancement of Artificial Intelligence, 26--32.Google Scholar
- Peter Li, Jiejun Xu, and Tsai-Ching Lu. 2015. Leveraging Homophily to Infer Demographic Attributes: Inferring the Age of Twitter Users Using Label Propagation. In Proceedings of Workshop on Information In Networks (WIN15).Google Scholar
- Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology 27, 1 (2001), 415--444.Google ScholarCross Ref
- Alan Mislove, Sune Lehmann, Yong-Yeol Ahn, Jukka-Pekka Onnela, and J. Niels Rosenquist. 2011. Understanding the Demographics of Twitter Users. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM'11). Barcelona, Spain.Google Scholar
- Lewis Mitchell, Kameron Decker Harris, Morgan R. Frank, Peter Sheridan Dodds, and Christopher M. Danforth. 2013. The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. CoRR abs/1302.3299 (2013). http://dblp.uni-trier.de/db/journals/corr/corr1302.html#abs-1302-3299Google Scholar
- M. E. J. Newman. 2003. Mixing patterns in networks. Phys. Rev. E 67, 2 (Feb. 2003), 026126.Google ScholarCross Ref
- Mathieu Perreault and Derek Ruths. 2011. The Effect of Mobile Platforms on Twitter Content Generation. In International AAAI Conference on Web and Social Media. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2798Google Scholar
- Barbara Poblete, Ruth Garcia, Marcelo Mendoza, and Alejandro Jaimes. 2011. Do All Birds Tweet the Same?: Characterizing Twitter Around the World. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM '11). New York, NY, USA, 1025--1030. Google ScholarDigital Library
- Statista-The Statistics Portal. 2016. Statistics and facts about Smartphones. https://www.statista.com/topics/840/. (2016). Accessed: 2016-10-1.Google Scholar
- Statista-The Statistics Portal. 2016. Statistics and facts about Social Networks. http://www.statista.com/topics/1164/. (2016). Accessed: 2016-10-1.Google Scholar
- David Sayce. 2016. Number of tweets per day? http://www.dsayce.com/socialmedia/10-billions-tweets/. (2016). Accessed: 2016-10-1.Google Scholar
- Gang Wang, Konark Gill, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. 2013. Wisdom in the Social Crowd: An Analysis of Quora. In Proceedings of the 22Nd International Conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 1341--1352. Google ScholarDigital Library
- Jiejun Xu, Ryan Compton, Tsai-Ching Lu, and David Allen. 2014. Rolling Through Tumblr: Characterizing Behavioral Patterns of the Microblogging Platform. In Proceedings of the 2014 ACM Conference on Web Science (WebSci '14). ACM, New York, NY, USA, 13--22. Google ScholarDigital Library
- Jiejun Xu and Tsai-Ching Lu. 2015. Toward precise user-topic alignment in online social media. In Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 767--775. Google ScholarDigital Library
- Jiejun Xu, Tsai-Ching Lu, Ryan Compton, and David Allen. 2014. Civil unrest prediction: A tumblr-based exploration. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, 403--411.Google ScholarCross Ref
- Jiejun Xu, Tsai-Ching Lu, Ryan Compton, and David Allen. 2014. Quantifying cross-platform engagement through large-scale user alignment. In Proceedings of the 2014 ACM conference on Web science. ACM, 281--282. Google ScholarDigital Library
- Faiyaz Al Zamal, Wendy Liu, and Derek Ruths. 2012. Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors. In ICWSM, John G. Breslin, Nicole B. Ellison, James G. Shanahan, and Zeynep Tufekci (Eds.). The AAAI Press. http://dblp.uni-trier.de/db/conf/icwsm/icwsm2012.html#ZamalLR12Google Scholar
Index Terms
- Characterizing Regional and Behavioral Device Variations Across the Twitter Timeline: A Longitudinal Study
Recommendations
Russian trolls speaking Russian: Regional Twitter operations and MH17
WebSci '20: Proceedings of the 12th ACM Conference on Web ScienceThe role of social media in promoting media pluralism was initially viewed as wholly positive as social media could break the oligopoly of (often state-owned) mainstream media. However, some governments are allegedly manipulating social media by hiring ...
A sentiment analysis of audiences on twitter: who is the positive or negative audience of popular twitterers?
ICHIT'11: Proceedings of the 5th international conference on Convergence and hybrid information technologyMicroblogging is a new informal communication medium of blogging that differs from a traditional blog in which content is much shorter. Microbloggers post about topics that describe their current status. Twitter is a popular microblogging service and ...
Information resonance on Twitter: watching Iran
SOMA '10: Proceedings of the First Workshop on Social Media AnalyticsTwitter has undoubtedly caught the attention of both the general public, and academia as a microblogging service worthy of study and attention. Twitter has several features that sets it apart from other social media/networking sites, including its 140 ...
Comments