White, man, and highly followed: gender and race inequalities in Twitter

Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.

[1]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

[2]  Krishna P. Gummadi,et al.  Inferring who-is-who in the Twitter social network , 2012, WOSN '12.

[3]  Júlio Cesar dos Reis,et al.  Demographics of News Sharing in the U.S. Twittersphere , 2017, HT.

[4]  Krishna P. Gummadi,et al.  Forgetting in Social Media: Understanding and Controlling Longitudinal Exposure of Socially Shared Data , 2016, SOUPS.

[5]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[6]  D. Barker.  Global gender disparities in science , 2013 .

[7]  Markus Strohmaier,et al.  Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods , 2016, WWW.

[8]  Supun Chathuranga Nakandala,et al.  Gendered Conversation in a Social Game-Streaming Platform , 2016, ICWSM.

[9]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[10]  Stanislav D. Dobrev,et al.  Unequal on top: Gender profiling and the income gap among high earner male and female professionals. , 2015, Social science research.

[11]  Janine Willis,et al.  First Impressions , 2006, Psychological science.

[12]  Saeideh Bakhshi,et al.  "I need to try this"?: a statistical overview of pinterest , 2013, CHI.

[13]  D. Ruths,et al.  What's in a Name? Using First Names as Features for Gender Inference in Twitter , 2013, AAAI Spring Symposium: Analyzing Microtext.

[14]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

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

[16]  Yong-Yeol Ahn,et al.  Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility , 2016, ICWSM.

[17]  Michael Luca,et al.  Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment , 2016 .

[18]  Christopher R. Knittel,et al.  Racial and Gender Discrimination in Transportation Network Companies , 2016 .

[19]  Cameron Blevins,et al.  Jane, John ... Leslie? A Historical Method for Algorithmic Gender Prediction , 2015, Digit. Humanit. Q..

[20]  Vincent Larivière,et al.  On the relationship between gender disparities in scholarly communication and country-level development indicators , 2015 .

[21]  Krishna P. Gummadi,et al.  Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations , 2017, ICWSM.

[22]  Fabrício Benevenuto,et al.  Towards sentiment analysis for mobile devices , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[23]  Fabrício Benevenuto,et al.  Reverse engineering socialbot infiltration strategies in Twitter , 2014, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[24]  Mounia Lalmas,et al.  First Women, Second Sex: Gender Bias in Wikipedia , 2015, HT.

[25]  Huan Liu,et al.  When is it biased?: assessing the representativeness of twitter's streaming API , 2014, WWW.

[26]  Fabrício Benevenuto,et al.  You followed my bot! Transforming robots into influential users in Twitter , 2013, First Monday.

[27]  Krishna P. Gummadi,et al.  Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media , 2017, CSCW.

[28]  Krishna P. Gummadi,et al.  Geographic Dissection of the Twitter Network , 2012, ICWSM.

[29]  Daniel M. Romero,et al.  Influence and passivity in social media , 2010, ECML/PKDD.

[30]  Jisun An,et al.  #greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter , 2016, ICWSM.

[31]  E. Bonilla-Silva.  Racism without racists : color-blind racism and the persistence of racial inequality in the United States , 2006 .

[32]  Eduardo Graells-Garrido,et al.  Women through the glass ceiling: gender asymmetries in Wikipedia , 2016, EPJ Data Science.

[33]  V. Larivière.  Global gender disparities in science , 2013 .

[34]  Yuning Jiang,et al.  Learning Deep Face Representation , 2014, ArXiv.

[35]  Fabrício Benevenuto,et al.  From migration corridors to clusters: The value of Google+ data for migration studies , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[36]  M. Williams,et al.  Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data , 2015, PloS one.

[37]  Virgílio A. F. Almeida,et al.  Understanding factors that affect response rates in twitter , 2012, HT '12.

[38]  Cassidy R. Sugimoto,et al.  Bibliometrics: Global gender disparities in science , 2013, Nature.

[39]  Thomas M. Shapiro,et al.  Black Wealth, White Wealth: A New Perspective on Racial Inequality. , 1995 .

[40]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[41]  Ana-Maria Popescu,et al.  Democrats, republicans and starbucks afficionados: user classification in twitter , 2011, KDD.

[42]  Aron Culotta,et al.  Predicting the Demographics of Twitter Users from Website Traffic Data , 2015, AAAI.

[43]  Seth Ovadia,et al.  The Glass Ceiling Effect , 2001 .

[44]  A. J. Morales,et al.  Efficiency of human activity on information spreading on Twitter , 2014, Soc. Networks.

[45]  Fusheng Wang,et al.  A Comparative Study of Demographic Attribute Inference in Twitter , 2015, ICWSM.

[46]  Fabrício Benevenuto,et al.  Linguistic Diversities of Demographic Groups in Twitter , 2017, HT.

[47]  Duncan J. Watts,et al.  Who says what to whom on twitter , 2011, WWW.

[48]  Venkata Rama Kiran Garimella,et al.  Inferring international and internal migration patterns from Twitter data , 2014, WWW.