Identifying Locus of Control in Social Media Language

Individuals express their locus of control, or “control”, in their language when they identify whether or not they are in control of their circumstances. Although control is a core concept underlying rhetorical style, it is not clear whether control is expressed by how or by what authors write. We explore the roles of syntax and semantics in expressing users’ sense of control –i.e. being “controlled by” or “in control of” their circumstances– in a corpus of annotated Facebook posts. We present rich insights into these linguistic aspects and find that while the language signaling control is easy to identify, it is more challenging to label it is internally or externally controlled, with lexical features outperforming syntactic features at the task. Our findings could have important implications for studying self-expression in social media.

[1]  Lyle H. Ungar,et al.  Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions , 2017, IJCNLP.

[2]  Gower Street,et al.  Health inequalities among British civil servants: the Whitehall II study , 1991, The Lancet.

[3]  Jon M. Kleinberg,et al.  Echoes of power: language effects and power differences in social interaction , 2011, WWW.

[4]  Slav Petrov,et al.  Globally Normalized Transition-Based Neural Networks , 2016, ACL.

[5]  R. Kessler,et al.  The MIDUS National Survey: An Overview. , 2004 .

[6]  Noah A. Smith,et al.  A Dependency Parser for Tweets , 2014, EMNLP.

[7]  Lyle H. Ungar,et al.  Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference , 2016, EMNLP.

[8]  Maarten Sap,et al.  DLATK: Differential Language Analysis ToolKit , 2017, EMNLP.

[9]  Nikolaos Aletras,et al.  An analysis of the user occupational class through Twitter content , 2015, ACL.

[10]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[11]  Lyle H. Ungar,et al.  Modeling and Visualizing Locus of Control with Facebook Language , 2018, ICWSM.

[12]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[13]  M. Bradley,et al.  Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .

[14]  Andrew Y. Ng,et al.  Parsing with Compositional Vector Grammars , 2013, ACL.

[15]  Sharath Chandra Guntuku,et al.  Facebook versus Twitter: Cross-Platform Differences in Self-Disclosure and Trait Prediction , 2018 .

[16]  N. Fairclough,et al.  Language and Power , 2009 .

[17]  Lyle H. Ungar,et al.  Diachronic degradation of language models: Insights from social media , 2018, ACL.

[18]  R. Kessler,et al.  Employer burden of mild, moderate, and severe major depressive disorder: mental health services utilization and costs, and work performance , 2010, Depression and anxiety.

[19]  Beth Levin,et al.  English Verb Classes and Alternations: A Preliminary Investigation , 1993 .

[20]  James K. Harter,et al.  Well-being in the workplace and its relationship to business outcomes: A review of the Gallup studies. , 2003 .

[21]  S. Srivastava,et al.  The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. , 1999 .

[22]  J. Rotter.  Generalized expectancies for internal versus external control of reinforcement. , 1966, Psychological monographs.

[23]  Roger Levy,et al.  Negotiating Lexical Uncertainty and Speaker Expertise with Disjunction , 2015 .

[24]  Benjamin,et al.  Facebook VS．亚洲社会网络 , 2008 .

[25]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[26]  Eric Horvitz,et al.  Identifying Dogmatism in Social Media: Signals and Models , 2016, EMNLP.

[27]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .