ABSTRACT
How do users on social platforms consume content shared by their friends? Is this consumption socially motivated, and can we predict it? Considerable prior work has focused on inferring and learning user preferences with respect to broadcasted, or open-network content in public spheres like webpages or public videos. However, user engagement with narrowcasted, closed-network content shared by their friends is considerably under-explored, despite being a commonplace activity. Here we bridge this gap by focusing on consumption of visual media content in closed-network settings, using data from Snapchat, a large multimedia-driven social sharing service with over 200M daily active users. Broadly, we answer questions around content consumption patterns, social factors that are associated with such consumption habits, and predictability of consumption time. We propose models for patterns in users' time-spending behaviors across friends, and observe that viewers preferentially and consistently spend more time on content from certain friends, even without considering any explicit notion of intrinsic content value. We also find that consumption time is highly correlated with several engagement-based social factors, suggesting a large social role in closed-network content consumption. Finally, we propose a novel approach of modeling future consumption time as a learning-to-rank task over users? friends. Our results demonstrate significant predictive value (0.815 P@1, 0.650 nDCG@10) using only social factors. We expect our work to motivate additional research in modeling consumption and ranking of online closed-network content.
- Majid Alfifi, Parisa Kaghazgaran, James Caverlee, and Fred Morstatter. 2019. A large-scale study of ISIS social media strategy: Community size, collective influence, and behavioral impact. In ICWSM.Google Scholar
- Eytan Bakshy, Jake M Hofman, Winter A Mason, and Duncan J Watts. 2011. Everyone's an influencer: quantifying influence on twitter. In WSDM.Google Scholar
- Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The role of social networks in information diffusion. In www. ACM.Google Scholar
- Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H Chi. 2018. Latent cross: Making use of context in recurrent recommender systems. In WSDM.Google Scholar
- YouTube Offical Blog. 2017. You know what's cool? A billion hours.Google Scholar
- Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov. 2016. A context-aware time model for web search. In SIGIR. ACM.Google Scholar
- Christopher JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning (2010).Google Scholar
- Justin Cheng, Lada Adamic, P Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted?. In WWW.Google Scholar
- Aaron Clauset, Cosma Rohilla Shalizi, and Mark EJ Newman. 2009. Power-law distributions in empirical data. SIAM review (2009).Google Scholar
- Hana Habib, Neil Shah, and Rajan Vaish. 2019. Impact of Contextual Factors on Snapchat Public Sharing. In CHI. ACM.Google Scholar
- Liangjie Hong, Ovidiu Dan, and Brian D Davison. 2011. Predicting popular messages in twitter. In WWW.Google Scholar
- Parisa Kaghazgaran, James Caverlee, and Anna Squicciarini. 2018. Combating crowdsourced review manipulators: A neighborhood-based approach. In WSDM.Google Scholar
- Gerald Keller. 2015. Statistics for Management and Economics, Abbreviated .Cengage Learning.Google Scholar
- Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In KDD.Google Scholar
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media?. In WWW.Google Scholar
- Hemank Lamba and Neil Shah. 2019. Modeling Dwell Time Engagement on Visual Multimedia. In KDD. ACM.Google Scholar
- Dik L Lee, Huei Chuang, and Kent Seamons. 1997. Document ranking and the vector-space model. IEEE software (1997).Google ScholarDigital Library
- Kristina Lerman and Rumi Ghosh. 2010. Information contagion: An empirical study of the spread of news on digg and twitter social networks. In ICWSM.Google Scholar
- Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In www. ACM.Google Scholar
- David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. JASIST (2007).Google Scholar
- Chao Liu, Ryen W White, and Susan Dumais. 2010. Understanding web browsing behaviors through Weibull analysis of dwell time. In SIGIR. ACM.Google Scholar
- Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval (2009).Google Scholar
- Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King. 2011. Recommender systems with social regularization. In WSDM. ACM.Google Scholar
- V'ictor Mart'inez, Fernando Berzal, and Juan-Carlos Cubero. 2017. A survey of link prediction in complex networks. CSUR (2017).Google Scholar
- Rahmtin Rotabi, Krishna Kamath, Jon Kleinberg, and Aneesh Sharma. 2017. Detecting strong ties using network motifs. In www. ACM.Google Scholar
- Nikos Salamanos, Elli Voudigari, Theodore Papageorgiou, and Michalis Vazirgiannis. 2012. Discovering correlation between communities and likes in facebook. In ICGCC. IEEE.Google Scholar
- Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, and Christos Faloutsos. 2015. Timecrunch: Interpretable dynamic graph summarization. In KDD.Google ScholarDigital Library
- Parag Singla and Matthew Richardson. 2008. Yes, there is a correlation:-from social networks to personal behavior on the web. In www. ACM.Google Scholar
- Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. 2013. Exploiting local and global social context for recommendation. In JCAI. AAAI.Google Scholar
- Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, and Suhang Wang. 2020. Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. In KDD.Google Scholar
- TIME. 2016a. Heres How Much Time Snapchat Users Spend on the App. http://time.com/4272935/snapchat-users-usage-time-app-advertising/.Google Scholar
- TIME. 2016b. Instagram Just Hit the 500 Million User Mark. http://time.com/money/4376329/instagram-users/.Google Scholar
- Rongjing Xiang, Jennifer Neville, and Monica Rogati. 2010. Modeling relationship strength in online social networks. In WWW. ACM.Google Scholar
- Songhua Xu, Hao Jiang, and Francis Chi-Moon Lau. 2011. Mining user dwell time for personalized web search re-ranking. In IJCAI. AAAI.Google Scholar
- Peifeng Yin, Ping Luo, Wang-Chien Lee, and Min Wang. 2013. Silence is also evidence: interpreting dwell time for recommendation from psychological perspective. In KDD. ACM.Google Scholar
- Jerrold H Zar. [n.d.]. Spearman rank correlation. Wiley Online Library.Google Scholar
- Yuchen Zhang, Weizhu Chen, Dong Wang, and Qiang Yang. 2011. User-click modeling for understanding and predicting search-behavior. In KDD. 1388--1396.Google Scholar
- Yuan Zhang, Tianshu Lyu, and Yan Zhang. 2018. Cosine: Community-preserving social network embedding from information diffusion cascades. In AAAI.Google Scholar
Index Terms
- Social Factors in Closed-Network Content Consumption
Recommendations
Characterizing user behavior in online social networks
IMC '09: Proceedings of the 9th ACM SIGCOMM conference on Internet measurementUnderstanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first ...
User Modeling in Large Social Networks
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data MiningThis proposal aims to harness the power of data, social, and network sciences to model user behavior in social networks. Specifically, we focus on individual users and investigate the interplay between their behavior and subsequently emergent social ...
How exhibitionism and voyeurism contribute to engagement in SNS use: The mediating effects of content production and consumption
Highlights- Psychological traits such as exhibitionism and voyeurism can affects to social media use.
AbstractThis study explored the mediating effects of two patterns of content use (production and consumption) on the relationship between psychological traits (exhibitionism and voyeurism) and intensity of social networking service (SNS) use. ...
Comments