About this book

How can Twitter data be used to study societal-level phenomena? This book surveys cutting edge research which uses Twitter data to analyze  phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow’s stock prices.
Each chapter, written by leading domain experts in a clear and accessible language, takes the reader to the forefront of science, citing the latest developments for further technical details. Thus, this book makes the advances in computing domain as applied to Twitter accessible to readers who have not been exposed to formal training on how to analyze “big data”. Instead, we dedicate a chapter on “Twitter Data Analysis” to provide an overview of tools and skills required, and give pointers on how to get started if the reader wants to embark on similar research themselves.



Details about the organized chapters can be found below.

The book’s idea started as a 3-hour tutorial on “Twitter and the Real World” given in October 2013 at the Conference on Information and Knowledge Management (CIKM). Expanding on the topics covered in that tutorial, this book provides a wider thematic scope of research and the most recent associated scientific work. Some of the resources, such as slides, on the tutorial's page might still be of interest to the reader though.




 

 

 

 Yelena Mejova (@yelenamm) is a scientist in the Social Computing Group at Qatar Computing Research Institute. Specializing in text retrieval and mining, Yelena is interested in building tools for tracking real-life social phenomena in social media. Her work on sentiment classification and evaluation, as well as political opinion tracking and poll now-casting has appeared in international computer and web science conferences such as ICWSM, WebSci and WSDM, and she is a co-editor of a Social Science Computing Review special issue on “Quantifying Politics Using Online Data”.

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 Ingmar Weber (@ingmarweber) is a senior scientist in the Social Computing group at Qatar Computing Research Institute. As an undergraduate he studied mathematics at Cambridge University, UK, before moving to the Max-Planck Institute for Computer Science, Germany, for his PhD. Before moving to Qatar, he spent two years working at the École polytechnique fédérale de Lausanne, Switzerland, and three years at Yahoo! Research in Barcelona, Spain. In his research, he uses large amounts of online data from Twitter and other sources to study phenomena that affect society at large. Recent work has looked at political polarization in Egypt, at global gender inequality in online social networks, at international migration, and at food consumption and obesity seen through social media. His research is frequently featured in popular press such as the Washington Post, Forbes, NewScientist, Financial Times, or Foreign Policy. He occasionally writes about his work for Al Jazeera.

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 Michael Macy is Goldwin Smith Professor of Arts and Sciences and Director of the Social Dynamics Laboratory at Cornell, where he has worked since 1997. He received his B.A. and later Ph.D, along with an M.A. from Stanford. With support from the NSF, the Department of Defense, and Google, his research team has used computational models, online experiments, and mobile digital traces to explore enigmatic social patterns, such as circadian rhythms, the emergence and collapse of fads, the spread of self-destructive behaviors, cooperation in social dilemmas, the critical mass in collective action, the spread of contagions on small-world networks, the polarization of opinion, segregation of neighborhoods, and assimilation of minority cultures. Recent research uses 509 million Twitter messages to track mood changes, and telephone logs for 12B calls to measure the economic correlates of network structure. His research has been published in Science, PNAS, American Journal of Sociology, American Sociological Review, and Annual Review of Sociology.

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