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Abstract

The rising popularity of social media has created valuable opportunities for researchers to quickly and cheaply tap large amounts of data on naturalistic social interactions. One of the most useful and accessible sources of social media data is Twitter. Although Twitter data are free to access, however, the programming tools required to collect and analyze these data are likely to create a barrier for many psychologists. The goal of this article is to reduce that barrier by explaining what data are available from Twitter and providing code and step-by-step instructions to retrieve them. I also review approaches to deriving psychological insight from these data, the accompanying challenges, and potential solutions, providing code to make these tasks easier. Particularly for researchers without access to large participant pools, overseas collaborators, or online panels, Twitter can be an important source of psychological insight.

Keywords

Twitter methods language analysis social networks

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References

Barberá

Jost

J. T.

Nagler

Tucker

J. A.

(2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological, 10, 1531–1542.

Carpenter

Preotiuc-Pietro

Flekova

Giorgi

Hagan

Kern

M. L.

… Seligman

M. E. P.

(2016). Real men don’t say “‘cute’”: Using automatic language analysis to isolate inaccurate aspects of stereotypes. Social Psychological and Personality Science, 1–13.

Chen

E. E.

Wojcik

S. P.

(2016). A practical guide to big data research in psychology. Psychological Methods, 21, 458–474.

Dehghani

Johnson

Hoover

Sagi

Garten

Parmar

N. J.

… Graham

(2016). Purity homophily in social networks. Journal of Experimental Psychology. General, 145, 366–375.

De Choudhury

Gamon

Counts

Horvitz

(2013). Predicting depression via social media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 128–137). Cambridge, MA: AAAI Press.

Dodds

P. S.

Harris

K. D.

Kloumann

I. M.

Bliss

C. A.

Danforth

C. M.

(2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS One, 6, e26752.

Doré

Ort

Braverman

Ochsner

K. N.

(2015). Sadness shifts to anxiety over time and distance from the national tragedy in Newtown, Connecticut. Psychological Science, 26, 363–373.

Dos Reis

V. L.

Culotta

(2015). Using matched samples to estimate the effects of exercise on mental health from Twitter. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 182–188). Cambridge, MA: AAAI Press.

Eichstaedt

J. C.

Schwartz

H. A.

Kern

M. L.

Park

Labarthe

D. R.

Merchant

R. M.

… Seligman

M. E. P.

(2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26, 159–169.

10.

Garcia-Gavilanes

Quercia

Jaimes

(2013). Cultural dimensions in Twitter: Time, individualism and power. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 195–204). Cambridge, MA: AAAI Press.

11.

Golder

S. A.

Macy

M. W.

(2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333, 1878–1881.

12.

Jones

N. M.

Wojcik

S. P.

Sweeting

Silver

R. C.

(2016). Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses. Psychological Methods, 21, 526.

13.

Kern

M. L.

Park

Eichstaedt

J. C.

Schwartz

H. A.

Sap

Smith

L. K.

Ungar

L. H.

(2016). Gaining insights from social media language: Methodologies and challenges. Psychological Methods, 21, 507–525.

14.

Kosinski

Wang

Lakkaraju

Leskovec

(2016). Mining big data to extract patterns and predict real-life outcomes. Psychological Methods, 21, 493–506.

15.

Mohammad

S. M.

Turney

P. D.

(2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence: An International Journal, 29, 436–465.

16.

Morstatter

Pfeffer

Liu

Carley

K. M.

(2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s Firehose. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 400–408). Cambridge, MA: AAAI Press.

17.

Pennebaker

J. W.

Booth

R. J.

Boyd

R. L.

Francis

M. E.

(2015). Linguistic Inquiry and Word Count: LIWC2015. Austin, TX: Pennebaker Conglomerates. Retrieved from www.LIWC.net

18.

Pennebaker

J. W.

Boyd

R. L.

Jordan

Blackburn

(2015). The development and psychometric properties of LIWC2015. Austin: University of Texas at Austin.

19.

Pew Research Center. (2014). Social media update 2014. Retrieved from http://www.pewinternet.org/2015/01/09/social-media-update-2014/

20.

Qiu

Lin

Ramsay

Yang

(2012). You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, 46, 710–718.

21.

Quercia

Kosinski

Stillwell

Crowcroft

(2011). Our Twitter profiles, our selves: Predicting personality with Twitter. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing (pp. 180–185). Los Alamitos, CA: IEEE.

22.

Ritter

R. S.

Preston

J. L.

Hernandez

(2013). Happy tweets: Christians are happier, more socially connected, and less analytical than atheists on Twitter. Social Psychological and Personality Science. Retrieved from https://doi.org/10.1177/1948550613492345.

23.

Schwartz

H. A.

Eichstaedt

J. C.

Kern

M. L.

Dziurzynski

(2013). Characterizing geographic variation in well-being using tweets. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 583–591). Cambridge, MA: AAAI Press.

24.

Snefjella

Kuperman

(2015). Concreteness and psychological distance in natural language use. Psychological Science, 26, 1449–1460.

25.

Stephens

Poorthuis

(2015). Follow thy neighbor: Connecting the social and the spatial networks on Twitter. Computers, Environment and Urban Systems, 53, 87–95.

26.

Sylwester

Purver

(2015). Twitter language use reflects psychological differences between democrats and republicans. PLoS One, 10, e0137422.

27.

Tamburrini

Cinnirella

Jansen

V. A. A.

Bryden

(2015). Twitter users change word usage according to conversation-partner social identity. Social Networks, 40, 84–89.

28.

Wojcik

S. P.

Hovasapian

Graham

Motyl

Ditto

P. H.

(2015). Conservatives report, but liberals display, greater happiness. Science, 347, 1243–1246.

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A Hands-On Guide to Conducting Psychological Research on Twitter

Abstract

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