Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T09:42:58.716Z Has data issue: false hasContentIssue false

Twitter as Data

Published online by Cambridge University Press:  18 January 2018

Zachary C. Steinert-Threlkeld
Affiliation:
University of California, Los Angeles

Summary

The rise of the internet and mobile telecommunications has created the possibility of using large datasets to understand behavior at unprecedented levels of temporal and geographic resolution. Online social networks attract the most users, though users of these new technologies provide their data through multiple sources, e.g. call detail records, blog posts, web forums, and content aggregation sites. These data allow scholars to adjudicate between competing theories as well as develop new ones, much as the microscope facilitated the development of the germ theory of disease. Of those networks, Twitter presents an ideal combination of size, international reach, and data accessibility that make it the preferred platform in academic studies. Acquiring, cleaning, and analyzing these data, however, require new tools and processes. This Element introduces these methods to social scientists and provides scripts and examples for downloading, processing, and analyzing Twitter data.
Get access
Type
Element
Information
Online ISBN: 9781108529327
Publisher: Cambridge University Press
Print publication: 18 January 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acemoglu, Daron, Tahoun, Ahmed, and Hassan, Tarek A. (2014). “The Power of the Street: Evidence from Egypt’s Arab Spring,” NBER Working Paper No. 20665.Google Scholar
Adamic, Lada A. and Glance, Natalie (2005). “The Political Blogosphere and the 2004 U.S. Election: Divided They Blog.” In Proceedings of the 3rd International Workshop on Link Discovery, August 21–25, 2005, Chicago, IL, pp. 3643.Google Scholar
Aday, Sean, Freelon, Deen, Farrell, Henry, Lynch, Marc, and Sides, John (2012). “New Media and Conflict After the Arab Spring.” Technical Report, United States Institute of Peace, Washington, DC.Google Scholar
Aday, Sean, Farrell, Henry, Lynch, Marc, Sides, John, Kelly, John, and Zuckerman, Ethan (2010). “Blogs and Bullets: New Media in Contentious Politics.” Technical Report United States Institute of Peace, Washington, DC.Google Scholar
Analytics, Caerus (2015). “Open Event Data Alliance.” phoenixdata.org.Google Scholar
Jason, Anastasopoulos L., Badani, Dhruvil, Lee, Crystal, Ginosar, Shiry, and Williams, Jake (2016). “Photographic Home Styles in Congress: A Computer Vision Approach.” http://arxiv.org/abs/1611.09942.Google Scholar
Asur, Sitaram and Huberman, Bernardo A. (2010). “Predicting the Future with Social Media.” In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, pp. 492–99.Google Scholar
Bail, Christopher A. (2014). “The Cultural Environment: Measuring Culture with Big Data.” Theory and Society, 43(3–4), 465–82.Google Scholar
Bakshy, Eytan, Messing, Solomon, and Adamic, Lada (2015). “Exposure to Ideologically Diverse News and Opinion on Facebook.” Sciencexpress, 348(6239), 1160.Google Scholar
Barberá, Pablo (2014). “How Social Media Reduces Mass Political Polarization. Evidence from Germany, Spain, and the US.” Paper prepared for the 2015 APSA Conference.Google Scholar
Barberá, Pablo (2015). “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis, 23(August 2013), 7691.Google Scholar
Pablo, Barberá, Jost, John T., Nagler, Jonathan, Tucker, Joshua A., and Bonneau, Richard (2015a). “Tweeting from Left to Right: Is Online Political Communication More Than an Echo Chamber?Psychological science, 26(10),1531–42. www.ncbi.nlm.nih.gov/pubmed/26297377.Google Scholar
Barberá, Pablo, Wang, Ning, Bonneau, Richard, Jost, John T., Nagler, Jonathan, Tucker, Joshua, and González-Bailón, Sandra (2015b). “The Critical Periphery in the Growth of Social Protests.” PloS ONE 10(11), 115.Google Scholar
Barberá, Pablo, Bonneau, Richard, Egan, Patrick, Jost, John T., Nagler, Jonathan, and Tucker, Joshua (2014). “Leaders or Followers? Measuring Political Responsiveness in the US Congress Using Social Media Data.” Prepared for delivery at the Annual Meeting of the American Political Science Association, August 28–31, 2014.Google Scholar
Bastos, Marco T., Mercea, Dan, and Charpentier, Arthur (2015). “Tents, Tweets, and Events: The Interplay between Ongoing Protests and Social Media.” Journal of Communication 65(2), 320350.Google Scholar
Beieler, John (2013). “A Tutorial on Deploying and Using Amazon Eleastic Cloud Compute Clusters.” The Political Methodologist 20(2), 1621.Google Scholar
Berger, Daniel, Kalyanaraman, Shankar, and Linardi, Sera (2014). “Violence and Cell Phone Communication: Behavior and Prediction in Cote d’Ivoire.” Working paper.Google Scholar
Bergstrom, Kelly (2011). ““Don’t Feed the Troll”: Shutting Down Debate about Community Expectations on Reddit.com.” First Monday 16(8).Google Scholar
Bernstein, Joseph (2017). “Never Mind the Russians, Meet the Bot King Who Helps Trump Win Twitter.” www.buzzfeed.com/josephbernstein/from-utah-with-love?utm-term=.xqpxB9kRv#.tiDymBqG7.Google Scholar
Bhatia, Rahul (2016). “The Inside Story of Facebook’s Biggest Setback.” May 12. www.theguardian.com/technology/2016/may/12/facebook-free-basics-india-zuckerberg.Google Scholar
Steven, Bird, Klein, E., and Loper, E. (2009). “Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit.” O’Reilly Media, Inc.Google Scholar
Blumenstock, J., Cadamuro, G., and On, R. (2015). “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science 350(6264), 10731076.Google Scholar
Blumenstock, Joshua E (2011). “Using Mobile Phone Data to Measure the Ties Between Nations.” In Proceedings of the 2011 iConference, pp. 195202.Google Scholar
Blumenstock, Joshua E (2012). “Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from Rwanda.” Information Technology for Development 18(2), 107125.CrossRefGoogle Scholar
Bollen, Johan, Mao, Huina, and Zeng, Xiaojun (2011). “Twitter Mood Predicts the Stock Market.” Journal of Computational Science 2(1), 18.CrossRefGoogle Scholar
Robert M., Bond, Fariss, Christopher J., Jones, Jason J., Kramer, Adam D.I., Marlow, Cameron, Settle, Jaime E., and Fowler, James H. (2012). A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489(7415), 295298.Google Scholar
Borge-Holthoefer, Javier, Magdy, Walid, Darwish, Kareem, and Weber, Ingmar (2015). “Content and Network Dynamics behind Egyptian Political Polarization on Twitter.” In 18th Conference on Computer-Supported Cooperative Work and Social Computing, pp. 130.CrossRefGoogle Scholar
Boschee, Elizabeth, Lautenschlager, Jennifer, O’Brien, Sean, Shellman, Steve, Starz, James, and Ward, Michael (2015). “ICEWS Coded Event Data.” http://dx.doi.org/10.7910/DVN/28075.Google Scholar
Bourlai, Elli and Herring, Susan C. (2014). “Multimodal Communication on Tumblr: I Have So Many Feels!.” In Proceedings of the 2014 ACM Conference on Web Science, pp. 171175.CrossRefGoogle Scholar
Boyd, Dannah, Golder, Scott, and Lotan, Gilad (2010). “Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter.” In 43rd Hawaii International Conference on System Sciences. IEEE, pp. 110.Google Scholar
Budak, Ceren and Watts, Duncan (2015). “Dissecting the Spirit of Gezi: Influence vs. Selection in the Occupy Gezi Movement.” Sociological Science 2: 370397.Google Scholar
Bury, Rhiannon, Deller, Ruth, and Greenwood, Adam (2013). “From Usenet to Tumblr: The Changing Role of Social Media.” Participations 10(1), 299318.Google Scholar
Catanese, Salvatore A, De Meo, Pasquale, Ferrara, Emilio, Fiumara, Giacomo, and Provetti, Alessandro (2011). “Crawling Facebook for Social Network Analysis Purposes.” In Proceedings of the International Conference on Web Intelligence, Mining and Semantics. New York.Google Scholar
Cavnar, W. B. and Trenkle, J. M. (1994). “n-Gram-Based Text Categorization.” In 3rd Annual Symposium on Document Analysis and Information Retrieval. Las Vegas, pp. 161175.Google Scholar
Chang, Yi, Tang, Lei, Inagaki, Yoshiyuki, and Liu, Yan (2014). “What is Tumblr: A Statistical Overview and Comparison.” SIGKDD Explorations 16(1), 2130.Google Scholar
Charles-Smith, Lauren E., Reynolds, Tera L., Cameron, Mark A., Mike Conway, Eric H. Lau, Y., Olsen, Jennifer M., Pavlin, Julie A., Mika Shigematsu, Laura C. Streichert, Katie J. Suda, and Corley, Courtney D. (2015). “Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review.” PLOS One 10(10), e0139701.Google Scholar
Cheng, Zhiyuan, Caverlee, James, and Lee, Kyumin (2010). “You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users.” In ACM International Conference on Information and Knowledge Management. Toronto.Google Scholar
Christia, Fotini, Yao, Leon, Wittels, Stephen, and Leskovec, Jure (2015). “Yemen Calling: Seven Things Cell Data Reveal about Life in the Republic.” Foreign Affairs. www.foreignaffairs.com/articles/yemen/2015–07-06/yemen-calling.Google Scholar
Conover, M.D., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., and Menczer, F. (2011). “Political Polarization on Twitter.” In Fifth International AAAI Conference on Weblogs and Social Media, pp. 8996.Google Scholar
Conover, Michael D., Gonçalves, Bruno, Flammini, Alessandro and Menczer, Filippo (2012). “Partisan Asymmetries in Online Political Activity.” EPJ Data Science 1(1), 119.Google Scholar
Conover, Michael D, Davis, Clayton, Ferrara, Emilio, McKelvey, Karissa, Menczer, Filippo, and Flammini, Alessandro (2013). “The Geospatial Characteristics of a Social Movement Communication Network.” PloS one 8(3), e55957.CrossRefGoogle ScholarPubMed
Coppock, Alexander, Guess, Andrew, and Ternovski, John (2016). “When Treatments are Tweets: A Network Mobilization Experiment over Twitter.” Political Behavior 38(1), 105128. http://dx.doi.org/10.1007/s11109-015-9308-6.Google Scholar
Dalton, Russell J., Greene, Steven, Beck, Paul Allen, and Huckfeldt, Robert (2002). “The Social Calculus of Voting: Interpersonal, Media, and Organizational Influences on Presidential Choices.” The American Political Science Review 96(1), 5773.Google Scholar
Davenport, Christian and Ball, Patrick (2002). “Views to a Kill: Exploring the Implications of Source Selection in the Case of Guatemalan State Terror, 1977–1995).” Journal of Conflict Resolution 46(3), 427450.Google Scholar
Diaz, Fernando, Gamon, Michael, Hofman, Jake, Kiciman, Emre, and Rothschild, David (2016). “Online and Social Media Data as a Flawed Continuous Panel Survey.” PLoS One 11(1), e0145406.Google Scholar
Peter Sheridan, Dodds, Decker Harris, Kameron, Kloumann, Isabel M., Bliss, Catherine A., and Danforth, Christopher M. (2011). “Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter.” PLoS ONEcomput 6(12), e26752.Google Scholar
Douglass, Rex W, Meyer, David a, Ram, Megha, Rideout, David, and Song, Dongjin (2015). “High Resolution Population Estimates from Telecommunications data.” EPJ Data Science 4(1), 4.Google Scholar
Dowle, Matt, Short, T, Lianoglou, S, and Srinivasan, A (2015). “data.table: Extension of data.frame.” https://cran.r-project.org/web/packages/data.table/index.html.Google Scholar
Driscoll, Jesse and Steinert-Threlkeld, Zachary C. (2017). “Structure, Agency, Hegemony, and Action: Ukrainian Nationalism in East Ukraine.” Working paper.Google Scholar
Dunbar, R. I. M (2011). “Constraints on the Evolution of Social Institutions and Their Implications for Information Flow.” Journal of Institutional Economics 7(03), 345371. www.journals.cambridge.org/abstracES1744137410000366.Google Scholar
Dunbar, R. I. M. (1995). “Neocortex Size and Group Size In Primates: A Test of the Hypothesis.” Journal of Human Evolution 28(3), 287296.Google Scholar
Dunbar, R.I.M., Arnaboldi, Valerio, Conti, Marco, and Passarella, Andrea (2015). “The Structure of Online Social Networks Mirrors Those in the Offline World.” Social Networks 43: 3947.Google Scholar
Eubank, Nicholas (2016). “Social Networks and the Political Salience of Ethnicity.” Working paper.Google Scholar
Evans, Heather K., Cordova, Victoria, and Sipole, Savannah (2014). “Twitter Style: An Analysis of How House Candidates Used Twitter in Their 2012 Campaigns.” PS: Political Science & Politics 47(02), 454462.Google Scholar
Farrell, Henry (2012). “The Consequences of the Internet for Politics.” Annual Review of Political Science 15(1), 3552.Google Scholar
Ferrara, Emilio (2012). “A Large-Scale Community Structure Analysis in Facebook.” EPJ Data Science 1(9), 130.CrossRefGoogle Scholar
Ferrara, Emilio and Bessi, Alessandro (2016). “Social Bots Distort the 2016 US Presidential Election Online Discussion.” First Monday 21(11), 117.Google Scholar
Ferrara, Emilio, Varol, Onur, Davis, Clayton, Menczer, Filippo, and Flammini, Alessandro (2016a. “BotOrNot: A System to Evaluate Social Bots.” In Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273274.Google Scholar
Ferrara, Emilio, Varol, Onur, Davis, Clayton, Menczer, Filippo, and Flammini, Alessandro (2016b). “The Rise of Social Bots.” Communications of the ACM 59(7), 96104.Google Scholar
Ferrara, Emilio, Interdonato, Roberto, and Tagarelli, Andrea (2014). “Online Popularity and Topical Interests through the Lens of Instagram.” ACM Hypertext 2014, 11.Google Scholar
Forelle, Michelle C, Howard, Philip N., Monroy-Hernandez, Andres, and Savage, Saiph (2015). “Political Bots and the Manipulation of Public Opinion in Venezuela.” SSRN Electronic Journal, pp. 18.Google Scholar
Fowler, James and Steinert-Threlkeld, Zachary C. (2016). “Online and Offline Activism in Egypt and Bahrain.” Technical report United States Agency for International Development. www.iie.org/en/Research-and-Publications/Publications-and-Reports/IIE-Bookstore/DFG-UCSD-Publication#.V-MIM5MrKqA.Google Scholar
Frank, Morgan R, Mitchell, Lewis, Dodds, Peter Sheridan, and Danforth, Christopher M (2013). “Happiness and the Patterns of Life: A Study of Geolocated Tweets.” Scientific Reports 3:2625.Google Scholar
Freelon, Dean (2012). “Arab Spring Twitter Data Now Available (sort of).” http://dfreelon.org/2012/02/11/arab-spring-twitter-data-now-available-sort-of.Google Scholar
Gao, Qi, Abel, Fabian, Houben, Geert-Jan, and Yong, Yu (2012). “A Comparative Study of Users’ Mircroblogging Behavior on Sina Weibo and Twitter.” In Proceedings of International Conference on user Modelling and Personalization (UMAP2012), pp.88101.Google Scholar
Manuel, Garcia-Herranz, Moro, Esteban, Cebrian, Manuel, Christakis, Nicholas A., and Fowler, James H. (2014). “Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks.” PloS ONE 9(4), e92413.Google Scholar
Gayo-Avello, Daniel (2013). “A Meta-Analysis of State-of-the-Art Electoral Prediction from Twitter Data.” Social Science Computer Review 31(6), 649679.Google Scholar
Gerber, Matthew S. (2014). “Predicting Crime Using Twitter and Kernel Density Estimation.” Decision Support Systems 61:115125.Google Scholar
Gilbert, Eric (2013). “Widespread Underprovision on Reddit.” In Proceedings of the 2013 Conference on Computer Supported Cooperative Work. New York: ACM Press p. 803.Google Scholar
Gjoka, Minas U. Irvine, C., and Butts, Carter T. (2010). “Walking in Facebook: A Case Study of Unbiased Sampling of OSNs.” In INFOCOM. San Diego, CA.Google Scholar
Golder, Scott A. and Macy, Michael W. (2011). “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength across Diverse Cultures.” Science (New York, N.Y.) 333(6051), 1878–81.Google Scholar
Golder, Scott A. and Macy, Michael W. (2014). “Digital Footprints: Opportunities and Challenges for Online Social Research.” Annual Review of Sociology 40(1), 129152.Google Scholar
Gonçalves, Bruno, Perra, Nicola, and Vespignani, Alessandro (2011). “Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number.” PloS ONE 6(8), e22656.Google Scholar
González-Bailón, Sandra, Borge-Holthoefer, Javier, Rivero, Alejandro, and Moreno, Yamir (2011). “The Dynamics of Protest Recruitment through an Online Network.” Scientific Reports 1:197.Google Scholar
Gonzalez-Bailon, Sandra, Borge-Holthoefer, Javier, and Moreno, Yamir (2013). “Broadcasters and Hidden Influentials in Online Protest Diffusion.” American Behavioral Scientist 57(7), 943965.Google Scholar
González-Bailón, Sandra, Wang, Ning, Rivero, Alejandro, Borge-Holthoefer, Javier, and Moreno, Yamir (2012). “Assessing the Bias in Communication Networks Sampled from Twitter.”Google Scholar
Greenwood, Shannon, Perrin, Andrew, and Duggan, Maeve (2016). “Social Media Update 2016.” Pew Research Center.Google Scholar
Grimmer, J. and Stewart, B. M. (2013). “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3), 267297.Google Scholar
Groshek, Jacob (2015). “Status Update on the BU-TCAT.” www.jgroshek.org/blog/2015/8/17/status-update-on-the-bu-tcat.Google Scholar
Guan, Wanqiu, Gao, Haoyu, Yang, Mingmin, Yuan, Li, Haixin, Ma, Qian, Weining, Cao, Zhigang, and Yang, Xiaoguang (2014). “Analyzing User Behavior of the Micro-Blogging Website Sina Weibo during Hot Social Events.” Physica A: Statistical Mechanics and its Applications 395:340351.Google Scholar
Alexander, Halavais (2011). “Social Science: Open Up Online Research.” Nature 48, 174175.Google Scholar
Hale, Scott A., Gaffney, Devin, and Graham, Mark (2011). “Where in the World Are You? Geolocation and Language Identification in Twitter.” The Professional Georgrapher 66(4).Google Scholar
Hammond, Jesse and Weidmann, Nils B. (2014). “Using Machine-Coded Event Data For The Micro-Level Study Of Political Violence.” Research & Politics 1(2), 18.Google Scholar
Han, Bo and Baldwin, Timothy (2011). “Lexical Normalisation of Short Text Messages: Makn Sens a #twitter.” In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Porland: Association for Computational Linguistics, pp. 368378.Google Scholar
Hassid, Jonathan (2012). “Safety Valve or Pressure Cooker? Blogs in Chinese Political Life.” Journal of Communication 62(2), 212230.Google Scholar
Hayden, Erika Check (2013). “Guidance Issued for US Internet Research: Institutional Review Boards May Need to Take a Closer Look at Some Types of Online Research.” www.nature.com/news/guidance-issued-for-us-internet-research-1.12860.Google Scholar
Hecht, Brent, Hong, Lichan, Suh, Bongwon, and Chi, Ed H. (2011). “Tweets from Justin Bieber’s Heart: The Dynamics of the Location Field in User Profiles.” In ACM Conference on Human Factors in Computing Systems. Number Figure 1 Vancouver:.Google Scholar
Hemphill, Libby, Otterbacher, Jahna, and Shapiro, Matthew (2013). “What’s Congress Doing on Twitter?” In Proceedings of the 2013 conference on Computer Supported Cooperative Work, pp. 877886.Google Scholar
Henrich, Joseph, Heine, Steven J., and Norenzayan, Ara (2010). “The Weirdest People in the World.” The Behavioral and Brain Sciences 33(2–3), 6183; discussion 83135.Google Scholar
Hochman, Nadav and Manovich, Lev (2013). “Zooming into an Instagram City: Reading the Local Through Social Media.” First Monday 18(7), 137.Google Scholar
Honeycutt, Courtenay and Herring, Susan C. (2009). “Beyond Microblogging: Conversation and Collaboration via Twitter.” In Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 110.Google Scholar
Hu, Yuheng, Manikonda, Lydia, and Kambhampati, Subbarao (2014). “What we Instagram: A First Analysis of Instagram Photo Content and User Types.” In Proceedings of the Eight International AAAI Conference on Weblogs and Social Media, pp. 595598.Google Scholar
Jones, Harvey and Jose Soltren, Hiram (2005). “Facebook: Threats to privacy.” Project MAC: MIT Project on Mathematics and Computing 1:176.Google Scholar
Jungherr, Andreas (2014). “Twitter in Politics: A Comprehensive Literature Review.”Google Scholar
Kallus, Nathan (2013). “Predicting Crowd Behavior with Big Public Data.” In 23rd International Conference on World Wide Web.Google Scholar
Kalyvas, Stathis N (2004). The Urban Bias in Research on Civil Wars. Vol. 13.Google Scholar
Kaneko, Takamu and Yanai, Keiji (2013). “Visual Event Mining from Geo-Tweet Photos.” In IEEE International Conference on Multimedia and Expo Workshops, pp. 16.Google Scholar
King, Gary, Pan, Jennifer, and Roberts, Margaret E. (2014). “Reverse-Engineering Censorship in China: Randomized Experimentation and Participant Observation.” Science 345(6199), 110.Google Scholar
Gary, King, Pan, Jennifer, and Roberts, Margaret E. (2016). “How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument.” http://gking.harvard.edu/50c?platform=hootsuite.Google Scholar
Kramer, Adam D.I., Guillory, Jamie E., and Hancock, Jeffrey T. (2014). “Experimental evidence of massive-scale emotional contagion through social networks.” In Proceedings of the National Academy of Sciences 111(24), 87888790.CrossRefGoogle ScholarPubMed
Kulshrestha, Juhi, Kooti, Farshad, Nikravesh, Ashkan, and Gummadi, Krishna P (2012). “Geographic Dissection of the Twitter Network.” In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, pp. 202209.Google Scholar
Kwak, Haewoon, Lee, Changhyun, Park, Hosung, and Moon, Sue (2010). “What Is Twitter, a Social Network or a News Media?” In International World Wide Conference. Raleigh: ACM Press, pp. 591600.Google Scholar
La Due, Lake, Ronald and Huckfeldt, Robert (1998). “Social Capital, Social Networks, and Political Participation.” Political Psychology 19(3), 567584.Google Scholar
Lakkaraju, Himabindu, McAuley, Julian J., and Leskovec, Jure (2013). “What’s in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media.” In International Conference on Web and Social Media.Google Scholar
Lang, Duncan Temple and the CRAN team (2016). RCurl: General Network Client Interface for R. R package version 1.95-4.8. https://CRAN.R-project.org/package=RCurlGoogle Scholar
Larson, Jennifer M., Nagler, Jonathan, Ronen, Jonathan, and Tucker, Joshua A. (2016). “Social Networks and Protest Participation: Evidence from 130 Million Twitter Users.” Working paper.Google Scholar
Lazer, David, Brewer, Devon, Christakis, Nicholas, Fowler, James, and King, Gary (2009). “Life in the Network: The Coming Age of Computational Social Science.” Science 323(5915), 721723.Google Scholar
Leetaru, Kalev H., Wang, Shaowen, Cao, Guofeng, Padmanabhan, Anand, and Shook, Eric (2013). “Mapping the Global Twitter Heartbeat: The Geography of Twitter.” First Monday 18(5–6), 133.Google Scholar
Leetaru, Kalev and Schrodt, Philip (2013). “GDELT: Global Data on Events, Language, and Tone, 1979–2012.” International Studies Association Annual Conference.Google Scholar
Lewis, Kevin, Kaufman, Jason, Gonzalez, Marco, Wimmer, Andreas, and Christakis, Nicholas (2008). “Tastes, Ties, and Time: A New Social Network Dataset Using Facebook.com.” Social Networks 30(4), 330342. http://linkinghub.elsevier.com/retrieve/pii/S0378873308000385.Google Scholar
Lin, Chengfeng, Jianhua, He, Zhou, Yi, Yang, Xiaokang, Chen, Kai, and Song, Li (2013). “Analysis and Identification of Spamming Behaviors in Sina Weibo Microblog.” In Proceedings of the 7th Workshop on Social Network Mining and Analysis 13: 19.Google Scholar
Llorente, Alejandro, Garcia-Herranz, Manuel, Cebrian, Manuel, and Moro, Esteban (2014). “Social media fingerprints of unemployment.” http://arxiv.org/abs/1411.3140.Google Scholar
Lotan, Gilad, Ananny, Mike, Gaffney, Devin, Boyd, Danah, Pearce, Ian, and Graeff, Erhardt (2011). “The Revolutions Were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions Web.” International Journal of Communications 5:13751406.Google Scholar
Lucas, Christopher, Nielsen, Richard A., Roberts, Margaret E., Stewart, Brandon M., Storer, Alex, and Tingley, Dustin (2015). “Computer-Assisted Text Analysis for Comparative Politics.” Political Analysis 23(2), 254277.Google Scholar
Malik, Momin M., Nakos, Constantine, Lamba, Hemank, and Pfeffer, Jiirgen (2015). “Population Bias in Geotagged Tweets.” In 9th International AAAI Conference on Weblogs and Social Media. Oxford.Google Scholar
Malik, Momin M. and Pfeffer, Jurgen (2016). “A Macroscopic Analysis of News Content in Twitter.” Digital Journalism 0811(May), 125.Google Scholar
Christopher D, Manning. and Schutze, Hinrich (1999). Foundations of Statistical Natural Language Processing. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
Marwell, Gerald, Oliver, Pamela E., and Prahl, Ralph (1988). “Social Networks and Collective Action: A Theory of the Critical Mass.” American Journal of Sociology 94(3), 502534.Google Scholar
Masad, David (2013). “Studying the Syrian Civil War with GDELT.” The Monkey Cage. http://themonkeycage.org/2013/07/09/how-computers-can-help-us-track-violent-conflicts-including-right-now-in-syria/.Google Scholar
McAdam, Doug (1986). “Recruitment to High-Risk Activism: The Case of Freedom Summer.” American Journal of Sociology 92(1), 6490.Google Scholar
Ryan, McGrath (2015). “twython.” https://twython.readthedocs.io/en/latest/.Google Scholar
Wes, McKinney (2015). “pandas.” http://pandas.pydata.org/.Google Scholar
Metternich, Nils W., Dorff, Cassy, Gallop, Max, Weschle, Simon, and Ward, Michael D. (2013). “Antigovernment Networks in Civil Conflicts: How Network Structures Affect Conflictual Behavior.” American Journal of Political Science 57(4).Google Scholar
Mislove, Alan, Lehmann, Sune, Ahn, Yong-Yeol, Onnela, Jukka-Pekka, and Niels Rosenquist, J. 2011). “Understanding the Demographics of Twitter Users.” In Proceedings of the Fifth International AAI Conference on the Weblogs and Social Media, pp. 554557.Google Scholar
Mocanu, Delia, Baronchelli, Andrea, Perra, Nicola, Vespignani, Alessandro, Goncalves, Bruno, and Zhang, Qian (2013). “The Twitter of Babel: Mapping World Languages through Microblogging Platforms.” PLOS One 8(4), e61981.Google Scholar
Morstatter, Fred, Pfeffer, Jurgen, Carley, Kathleen M., and Liu, Huan (2013). “Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose.” In Association for the Advancement of Artificial Intelligence.Google Scholar
Mueller, Andreas (2015). “scikit-learn.” http://scikit-learn.org/stable/.Google Scholar
Munger, Kevin (2016). “Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment.” Political Behavior, pp. 121.Google Scholar
Mustafaraj, E. and Metaxas, Pt (2010). “From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search.” In WebSci10: Extending the Frontiers of Society On-Line. p. 317. http://repository.wellesley.edu/computersciencefaculty/9/.Google Scholar
Nguyen, Dong, Gravel, Rilana, Trieschnigg, Dolf, and Meder, Theo (2013). “”How Old Do You Think I Am ?: A Study of Language and Age in Twitter.” Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media.Google Scholar
Nickerson, David W. (2008). “Is Voting Contagious? Evidence from Two Field Experiments.” American Political Science Review 102(01), 4957.Google Scholar
Onuch, Olga (2015). “EuroMaidan Protests in Ukraine: Social Media Versus Social Networks.” Problems of Post-Communism 62(4), 217235.Google Scholar
Opp, Karl-Dieter and Gern, Christiane (1993). “Dissident Groups, Personal Networks, and Spontaneous Cooperation: The East German Revolution of 1989.” American Sociological Review 58(5), 659680.Google Scholar
Poblete, Barbara, Garcia, Ruth, Mendoza, Marcelo, and Jaimes, Alejandro (2011). “Do All Birds Tweet the Same? Characterizing Twitter Around the World Categories and Subject Descriptors.” In The 21st ACM Conference on Information and Knowledge Management, pp. 10251030.Google Scholar
Qu, Yan, Huang, Chen, Zhang, Pengyi, and Zhang, Jun (2011). “Microblogging after a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake.” In Computer Supported Cooperative Work. Hangzhou, China, pp. 2534.Google Scholar
Rahimi, Babak (2011). “The Agonistic Social Media: Cyberspace in the Formation of Dissent and Consolidation of State Power in Postelection Iran.” The Communication Review 14(3), 158178.Google Scholar
Ramakrishnan, Naren, Chang-tien, Lu, Huang, Bert, Srinivasan, Aravind, Trinh, Khoa, and Getoor, Lise (2014). “Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators.” In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York City: ACM Press, pp. 17991808.Google Scholar
Ratkiewicz, Jacob, Conover, Michael D., Meiss, Mark, Goncalves, Bruno, Flamini, Alessandro, and Menczer, Filippo (2011). “Detecting and Tracking Political Abuse in Social Media.” In International Conference on Web and Social Media, pp. 297304.Google Scholar
Reich, Stephanie M., Subrahmanyam, Kaveri, and Espinoza, Guadalupe (2012). “Friending, IMing, and hanging out Face-to-Face: Overlap in Adolescents’ Online and Offline Social Networks.” Developmental Psychology 48(2), 356368.Google Scholar
Reuter, Ora John and Szakonyi, David (2013). “Online Social Media and Political Awareness in Authoritarian Regimes.” British Journal of Political Science, pp. 123.Google Scholar
Roberts, Margaret E., Stewart, Brandon M., Tingley, Dustin, Lucas, Christopher, Leder-Luis, Jetson, Gadarian, Shana Kushner, Albertson, Bethany, and Rand, David G. (2014). “Structural Topic Models for Open-Ended Survey Responses.” American Journal of Political Science 58(4), 10641082.Google Scholar
Robertson, Jordan (2016). “How to Hack an Election.” Bloomberg Businessweek. www.bloomberg.com/features/2016-how-to-hack-an-election/.Google Scholar
Sakaki, Takeshi, Okazaki, Makoto, and Matsuo, Yutaka (2010). “Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors.” In International World Wide Web Conference, pp. 851860.Google Scholar
Seabold, Skipper and Perktold, Josepf (2014). “statstools.” https://pypi.python.org/pypi/statsmodels.Google Scholar
Shweder, Richard A. and Nisbett, Richard E. (2017). “Long-Sought Research Deregulation Is Upon Us: Don’t Squander the Moment.” The Chronicle for Higher Education, 12 March 2017.Google Scholar
Silva, Thiago H., Vaz De Melo, Pedro O.S., Almeida, Jussara M., Salles, Juliana, and Loureiro, Antonio A. F. (2013). “A Picture of Instagram Is Worth More than a Thousand Words: Workload Characterization and Application.” In 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 123132.Google Scholar
Sloan, Luke and Morgan, Jeffrey (2015). “Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter.” PLoS ONE 10(11), 115.Google Scholar
Sloan, Luke, Morgan, Jeffrey, Burnap, Pete, and Williams, Matthew (2015). “Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data.” PLoS ONE 10(3), 120.CrossRefGoogle ScholarPubMed
Social, We Are (2016). “Leading Social Networks Worldwide as of April 2016, Ranked by Number of Active Users.” www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.Google Scholar
Sriram, Bharath, Fuhry, David, Demir, Engin, Ferhatosmanoglu, Hakan, and Demirbas, Murat (2010). “Short Text Classification in Twitter to Improve Information Filtering.” In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval – SIGIR ’10. New York: ACM Press, pp. 841842.Google Scholar
Starbird, Kate and Palen, Ley (2010). “Pass It On?: Retweeting in Mass Emergency.” In Information Systems for Crisis Response and Management. December 2004, Seattle, pp. 110.Google Scholar
Stefanidis, Anthony, Crooks, Andrew, and Radzikowski, Jacek (2011). “Harvesting Ambient Geospatial Information from Social Media Feeds.” GeoJournal 78(2), 319338.Google Scholar
Steinert-Threlkeld, Zachary C (2016). “Replication Data for: Longitudinal Network Centrality Using Incomplete Data.” http://dx.doi.org/10.7910/DVN/KKWB4A.Google Scholar
Steinert-Threlkeld, Zachary C (2017a). “Longitudinal Network Analysis with Incomplete Data.” Political Analysis. DOI: https://doi.org/10.1017/pan.2017.6.Google Scholar
Steinert-Threlkeld, Zachary C (2017b). “Spontaneous Collective Action: Peripheral Mobilization during the Arab Spring.” American Political Science Review 111(02), 379403.Google Scholar
Steinert-Threlkeld, Zachary C., Mocanu, Delia, Vespignani, Alessandro, and Fowler, James (2015). “Online Social Networks and Offline Protest.” EPJ Data Science 4(1), 19.Google Scholar
Stone, Biz (2010). “Tweet Preservation.” https://blog.twitter.com/2010/tweet-preservation.Google Scholar
Suh, Bongwon, Hong, Lichan, Pirolli, Peter, and Ed Chi, H. (2010). “Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network.” In IEEE Second International Conference on Social Computing, pp. 177184.Google Scholar
Sun, Shengyun, Liu, Hongyan, Jun, He, and Xiaoyong, Du (2013). “Detecting Event Rumors on Sina Weibo Automatically.” In Web Technologies and Applications, pp. 120131.Google Scholar
Tucker, Joshua A., Nagler, Jonathan, Metzger, Megan MacDuffee, Duncan Penfold-Brown, Pablo Barberá, and Bonneau, Richard (2016). “Big Data, Social Media, and Protest: Foundations for a Research Agenda.” In Alvarez, R. Michael, Computational Social Science: Discovery and Prediction. Cambridge: Cambridge University Press, chapter 7, pp. 199224.Google Scholar
Tufekci, Zeynep (2014). “Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls Pre-print.” In Proceedings of the 8th International AAAI Conference on Weblogs and Social Media. Ann Arbor.Google Scholar
Tufekci, Zeynep and Wilson, Christopher (2012). “Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square.” Journal of Communication 62(2), 363379.Google Scholar
Tufekci, Zeynep and Freelon, Deen (2013). “Introduction to the Special Issue on New Media and Social Unrest.” American Behavioral Scientist 57(7), 843847.Google Scholar
Tumasjan, Andranik, Sprenger, Timm O., Sandner, Philipp G., and Welpe, Isabell M. (2010). “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment.” In Association for the Advancement of Artificial Intelligence, pp. 178185.Google Scholar
Ugander, Johan, Karrer, Brian, Backstrom, Lars, and Marlow, Cameron (2011). “The Anatomy of the Facebook Social Graph.” arXiv:1111.4503.Google Scholar
Update on the Twitter Archive At the Library of Congress (2013). Technical Report, January, Library of Congress Washington, DC.Google Scholar
Valkanas, George, Katakis, Ioannis, Gunopulos, Dimitrios, and Stefanidis, Antony (2014). “Mining Twitter Data with Resource Constraints.” In 2014 International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, pp. 157164.Google Scholar
Vieweg, Sarah, Hughes, Amanda L., Starbird, Kate, and Palen, Leysia (2010). “Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness.” In Human Factors in Computing Systems. Atlanta, pp. 10791088.Google Scholar
Weber, Ingmar, Kiran Garimella, Venkata R., and Batayneh, Alaa (2013). “Secular vs. Islamist Polarization in Egypt on Twitter.” In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 290297.Google Scholar
Weidmann, Nils B (2014). “On the Accuracy of Media-based Conflict Event Data.” Journal of Conflict Resolution 59(6), 11291149. http://jcr.sagepub.com/cgi/doi/10.1177/0022002714530431.Google Scholar
Weidmann, Nils B. and Ward, Michael D. (2010). “Predicting Conflict in Space and Time.” Journal of Conflict Resolution 54(6), 883901, http://jcr.sagepub.com/cgi/doi/10.1177/0022002710371669.Google Scholar
Wilson, R. E., Gosling, S. D., and Graham, L. T. (2012). “A Review of Facebook Research in the Social Sciences.” Perspectives on Psychological Science 7(3), 203220.Google Scholar
Woolley, Samuel C (2016). “Automating Power: Social Bot Interference in Global Politics.” First Monday 21(4), 113.Google Scholar
Xu, Jiejun, Tsai-Ching, Lu, Compton, Ryan, and Allen, David (2014). “Civil Unrest Prediction: A Tumblr-Based Exploration.” In Kennedy, William G., Agarwal, Nitin, and Yang, Shanchieh Jay Social Computing, Behavioral–Cultural Modeling and Prediction, Vol. 8393. Springer International Publishing, pp. 403411.Google Scholar
Yardi, Sarita and Boyd, Danah (2010). “Tweeting from the Town Square: Measuring Geographic Local Networks.” In Fourth International AAAI Conference on Weblogs and Social Media, pp. 194201.Google Scholar
Yazdani, Mehrdad and Manovich, Lev (2015). “Predicting Social Trends from Non-Photographic Images on Twitter.” In Proceedings – 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, pp. 16531660.Google Scholar
Lei, Yu, Louis, Asur, Sitaram, and Huberman, Bernardo A. (2012). “Artificial Inflation: The Real Story of Trends and Trend-Setters in Sina Weibo.” In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom), pp. 514519.Google Scholar
Zamal, Faiyaz Al, Liu, Wendy, and Ruths, Derek (2012). “Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors.” In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, pp. 387–90.Google Scholar
Zeitzoff, Thomas (2011). “Using Social Media to Measure Conflict Dynamics: An Application to the 2008–2009 Gaza Conflict.” Journal of Conflict Resolution 55(6), 938–69.Google Scholar
Zeitzoff, Thomas (2016). “Does Social Media Influence Conflict? Evidence from the 2012 Gaza Conflict.” Journal of Conflict Resolution, forthcoming. https://doi.org/10.1177/0022002716650925.Google Scholar
Zeitzoff, Thomas, Kelly, John, and Lotan, Gilad (2015). “Using Social Media to Measure Foreign Policy Dynamics: An Empirical Analysis of the Iranian–Israeli Confrontation (2012–13).” Journal of Peace Research 52(3), 368383.Google Scholar
Zheludev, Ilya, Smith, Robert, and Aste, Tomaso (2014). “When Can Social Media Lead Financial Markets?Scientific Reports 4(4213).Google Scholar
Zhou, W.-X., Sornette, D., Hill, Russell A., and Dunbar, R. I. M. (2005). “Discrete Hierarchical Organization of Social Group Sizes.” Proceedings. Biological Sciences/The Royal Society 272(1561). 439444.Google Scholar
Zickuhr, Kathryn (2013). “Location-Based Services.” Pew Research Center’s Internet & American Life 51 (September), 6569, www.pewinternet.org/2013/09/12/location-based-services/.Google Scholar
Zimmer, Michael (2015). “The Twitter Archive at the Library of Congress: Challenges for Information Practice and Information Policy”. First Monday 20(7), 112.Google Scholar

Save element to Kindle

To save this element to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Twitter as Data
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Twitter as Data
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Twitter as Data
Available formats
×