User engagement in online News: Under the scope of sentiment, interest, affect, and gaze

Online content providers, such as news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo News US, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors such as news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated predicted if we account for the sentimentality and polarity of the content as well as other factors that drive attention and inspire human curiosity.

[1]  Hsinchun Chen,et al.  Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums , 2008, TOIS.

[2]  J. Henderson Human gaze control during real-world scene perception , 2003, Trends in Cognitive Sciences.

[3]  Steven Skiena,et al.  Trading Strategies to Exploit Blog and News Sentiment , 2010, ICWSM.

[4]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[5]  Mounia Lalmas,et al.  On saliency, affect and focused attention , 2012, CHI.

[6]  R. Remington Attention and saccadic eye movements. , 1980, Journal of experimental psychology. Human perception and performance.

[7]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[8]  Elaine Toms,et al.  The development and evaluation of a survey to measure user engagement , 2010, J. Assoc. Inf. Sci. Technol..

[9]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[10]  J. Findlay,et al.  The Relationship between Eye Movements and Spatial Attention , 1986, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[11]  Irene Lopatovska Searching for good mood: Examining relationships between search task and mood , 2009, ASIST.

[12]  Berkant Barla Cambazoglu,et al.  A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish , 2012, ISCIS.

[13]  Sasha Blair-Goldensohn,et al.  Sentiment Summarization: Evaluating and Learning User Preferences , 2009, EACL.

[14]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[15]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[16]  Xue Bai,et al.  Predicting consumer sentiments from online text , 2011, Decis. Support Syst..

[17]  Wei Zhang,et al.  Opinion retrieval from blogs , 2007, CIKM '07.

[18]  D. Kahneman,et al.  Attention and Effort , 1973 .

[19]  K. Rayner Eye movements and visual cognition : scene perception and reading , 1992 .

[20]  Christopher D. Manning,et al.  Exploring Sentiment Summarization , 2004 .

[21]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[22]  Khurshid Ahmad,et al.  Sentiment Polarity Identification in Financial News: A Cohesion-based Approach , 2007, ACL.

[23]  J. Gwizdka,et al.  The role of subjective factors in the information search process , 2009 .

[24]  Elaine Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[25]  Steven Skiena,et al.  Identifying Differences in News Coverage between Cultural/Ethnic Groups , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[26]  M. de Rijke,et al.  Predicting the volume of comments on online news stories , 2009, CIKM.

[27]  B. Velichkovsky,et al.  On the control of visual fixation durations in free viewing of complex images , 2011, Attention, perception & psychophysics.

[28]  Maarten de Rijke,et al.  News Comments: Exploring, Modeling, and Online Prediction , 2010, ECIR.

[29]  Bernardo A. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.

[30]  Circulations, Revenues, and Profits in a Newspaper Market with Fixed Advertising Costs , 2009 .

[31]  J. Galtung,et al.  The Structure of Foreign News , 1965 .

[32]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[33]  Gabriella Kazai,et al.  Towards a science of user engagement (Position Paper) , 2011 .

[34]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[35]  Arvid Kappas,et al.  Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs , 2013, CICLing.

[36]  Steven Skiena,et al.  Large-Scale Sentiment Analysis for News and Blogs (system demonstration) , 2007, ICWSM.

[37]  Wenji Mao,et al.  Social Computing: From Social Informatics to Social Intelligence , 2007, IEEE Intell. Syst..

[38]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[39]  M H Fischer,et al.  An Investigation of Attention Allocation during Sequential Eye Movement Tasks , 1999, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[40]  Michelle L. Gregory,et al.  User-directed Sentiment Analysis: Visualizing the Affective Content of Documents , 2006 .

[41]  D. Shaw,et al.  Agenda setting function of mass media , 1972 .

[42]  Serge Fdida,et al.  Predicting the popularity of online articles based on user comments , 2011, WIMS '11.

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

[44]  Steven Skiena,et al.  Access: news and blog analysis for the social sciences , 2010, WWW '10.

[45]  Fabio Crestani,et al.  Investigating Learning Approaches for Blog Post Opinion Retrieval , 2009, ECIR.

[46]  Andreas Dengel,et al.  Eye tracking analysis of preferred reading regions on the screen , 2010, CHI Extended Abstracts.

[47]  Aristides Gionis,et al.  Answers, not links: extracting tips from yahoo! answers to address how-to web queries , 2012, WSDM '12.

[48]  Berkant Barla Cambazoglu,et al.  A large-scale sentiment analysis for Yahoo! answers , 2012, WSDM '12.

[49]  Razvan C. Bunescu,et al.  Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques , 2003, Third IEEE International Conference on Data Mining.

[50]  Katherine L. Milkman,et al.  Social Transmission, Emotion, and the Virality of Online Content , 2010 .

[51]  Heather L. O'Brien,et al.  Exploring user engagement in online news interactions , 2011, ASIST.

[52]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..

[53]  Jon M. Kleinberg,et al.  Does Bad News Go Away Faster? , 2011, ICWSM.

[54]  Steven Skiena,et al.  International Sentiment Analysis for News and Blogs , 2021, ICWSM.

[55]  Scott Counts,et al.  Taking It All In? Visual Attention in Microblog Consumption , 2021, ICWSM.

[56]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.