Yelena Mejova (@yelenamejova) is currently a Scientist in the Social Computing Group at the Qatar Computing Research Institute HBKU. Specializing in social media analysis and mining, her work concerns the quantification of health and wellbeing signals in social media, as well as tracking of social phenomena, including politics and news consumption. Recently, she co-edited a volume on the use of Twitter for social science in Twitter: A Digital Socioscope, and a special issue of Social Science Computer Review on Quantifying Politics Using Online Data.
Yelena’s work on Source
Articles by Yelena
Analyzing Emotional Language in 21 Million News Articles
How We Used Off-the-Shelf Tools to Study Bias and Emotion in NewsPosted on
All of us have encountered a particularly emotionally charged news article, with every word betraying the author’s bias. But a single reader would have to be a dedicated follower of a news organization to really understand how much opinion is habitually betrayed in its work. To find out how carefully major news organizations moderate their language in articles on controversial topics, we at Qatar Computing Research Institute (QCRI) used computational techniques to analyze millions of articles from 15 large news organizations in the U.S. Some agencies, we found, do not shy from emotion-laden and biased rhetoric—the Huffington Post and Washington Post, for example. But we also found that, on average, the use of highly emotional language is curbed, pointing to possible self-moderation.