Venting Weight: Analyzing the Discourse of an Online Weight Loss Forum

Online social communities are becoming increasingly popular platforms for people to share information, seek emotional support, and maintain accountability for losing weight. Studying the discourse in these communities can offer insights on how users benefit from using these applications. This paper presents an analysis of language and discourse patterns in forum posts by users who lose weight and keep it off versus users with fluctuating weight dynamics. In contrast to prior studies, we have access to the weekly self-reported check-in weights of users along with their forum posts. This paper also presents a study on how goal-oriented forums are different from general online forums in terms of language markers. Our results reveal dierences about how the types of posts made by users vary along with their weight-loss patterns. These insights are closely related to the power dynamics of social interactions and can enable better design ofweight-loss applications thereby contributing to a healthy society.

[1]  Rena R Wing,et al.  Teammates and Social Influence Affect Weight Loss Outcomes in a Team‐Based Weight Loss Competition , 2012, Obesity.

[2]  Cornelia Caragea,et al.  I want what i need!: analyzing subjectivity of online forum threads , 2012, CIKM.

[3]  R. Wing,et al.  Long-term weight loss maintenance. , 2005, The American journal of clinical nutrition.

[4]  Robert M. Schindler,et al.  Internet forums as influential sources of consumer information , 2001 .

[5]  A. Bambina.  Online Social Support: The Interplay of Social Networks and Computer-Mediated Communication , 2007 .

[6]  C. Hawn.  Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are reshaping health care. , 2009, Health affairs.

[7]  D. Lau,et al.  The obesity epidemic and its impact on hypertension. , 2012, The Canadian journal of cardiology.

[8]  Laura D. Russell,et al.  “The Secret is Out!” Supporting Weight Loss through Online Interaction , 2010 .

[9]  Mark Dredze,et al.  Separating Fact from Fear: Tracking Flu Infections on Twitter , 2013, NAACL.

[10]  Brian K. Kit,et al.  The Epidemiology of Obesity among Adults , 2014 .

[11]  Eric Horvitz,et al.  Predicting postpartum changes in emotion and behavior via social media , 2013, CHI.

[12]  N. Heaivilin,et al.  Public Health Surveillance of Dental Pain via Twitter , 2011, Journal of dental research.

[13]  Mark S. Ackerman,et al.  Activity Lifespan: An Analysis of User Survival Patterns in Online Knowledge Sharing Communities , 2010, ICWSM.

[14]  Jennifer Preece,et al.  The top five reasons for lurking: improving community experiences for everyone , 2004, Comput. Hum. Behav..

[15]  Amy Jo Kim,et al.  Community Building on the Web: Secret Strategies for Successful Online Communities , 2000 .

[16]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[17]  Laura Schweitzer,et al.  Advances In Kernel Methods Support Vector Learning , 2016 .

[18]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[19]  Dan Cosley,et al.  Think different: increasing online community participation using uniqueness and group dissimilarity , 2004, CHI.

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

[21]  Vicenç Gómez,et al.  Statistical analysis of the social network and discussion threads in slashdot , 2008, WWW.

[22]  Gaurav Dubey,et al.  Exploring the Relationship Between Changes in Weight and Utterances in an Online Weight Loss Forum: A Content and Correlational Analysis Study , 2014, Journal of medical Internet research.

[23]  Deborah Estrin,et al.  Small data, where n = me , 2014, Commun. ACM.

[24]  G. Colditz,et al.  The disease burden associated with overweight and obesity. , 1999, JAMA.

[25]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[26]  Jon M. Kleinberg,et al.  Characterizing and curating conversation threads: expansion, focus, volume, re-entry , 2013, WSDM.

[27]  Jure Leskovec,et al.  A computational approach to politeness with application to social factors , 2013, ACL.

[28]  P. Rogers,et al.  Reconceptualizing Politeness to Accommodate Dynamic Tensions in Subordinate-to-Superior Reporting , 2003 .

[29]  N. Sherwood,et al.  Long-term weight loss maintenance in the United States , 2010, International Journal of Obesity.

[30]  R K Deppe,et al.  Self-handicapping and intrinsic motivation: buffering intrinsic motivation from the threat of failure. , 1996, Journal of personality and social psychology.

[31]  Paul W. Ballantine,et al.  Help me, I'm fat! Social support in online weight loss networks , 2011 .

[32]  J Rodin,et al.  The dieting maelstrom. Is it possible and advisable to lose weight? , 1994, The American psychologist.

[33]  Arild Faxvaag,et al.  What Influences Patient Participation in an Online Forum for Weight Loss Surgery? A Qualitative Case Study , 2014, Interactive journal of medical research.

[34]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[35]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[36]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.

[37]  Cornelia Caragea,et al.  Thread Specific Features are Helpful for Identifying Subjectivity Orientation of Online Forum Threads , 2012, COLING.