Quantifying Political Leaning from Tweets and Retweets

By Felix Ming Fai Wong, Chee Wei Tan, Soumya Sen, Mung Chiang. 2013

95 citations recorded by the source corpus.

Media outlets and pundits have been quick to embrace online social networks to disseminate their own opinions. But pundits’ opinions and news coverage are often marked by a clear political bias, as widely evidenced during the fiercely contested2012 U.S. presidential elections. Given the wide availability of such data from sites like Twitter, a natural question is whether we can quantify the political leanings of media outlets using OSN data. In this work, by drawing a correspondence between tweeting and retweeting behavior, we formulate political leaning estimation as an ill-posed linear inverse problem. The result is a simple and scalable approach that does not require explicit knowledge of the network topology. We evaluate our method with a dataset of 119 million election-related tweets collected from April to November, and use it to study the political leaning of prominent tweeters and media sources.

Subfields: Communication, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Sociology and Political Science

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…(see also sections 2.2 and 6.1), studies often end up using varying deﬁnitions and proxies for the target variable (e.g., political leaning) and class labels (e.g., democrats or republicans), leading to results that are hard to compare or generalize (Cohen and Ruths, 2013; Wong et al., 2013).”
A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data (2012, background): “(2010) rather successfully correlated tweets with several public opinion time series; Tumasjan et al. (2010) claimed to have predicted the outcome of German elections; Bollen et al.”