Social Media News Communities:Gatekeeping, Coverage, and Statement

Bias

Diego Saez-Trumper∗1 Carlos Castillo† Mounia Lalmas‡

∗Universitat Pompeu Fabra, Barcelona†Qatar Computing Research Institute, Doha

‡Yahoo Labs London

San Francisco, October, 2013

1This work was done while visiting the Qatar Computing Research Institute

”Media bias refers to (...) the selection of which stories arereported and how they are reported”. S. Rivolta

Selection

Coverage

Statement

Selection

Coverage

Statement

Selection

Coverage

Statement

Selection

Coverage

Statement

Goal: quantify biases present in onlinenews

Challenges

I Consider a large set of news sources.I Compare news sources with social media (Twitter).I Use unsupervised methods.

Data set - News Sources

I Use the top-100 news websites from Alexa.com.I Download all the news they publish trough RSS and

Twitter.

Data set - Twitter

Download all tweets containing a URL pointing to a newssource.

Community 6= Followers

People who have tweeted at least K1 articles from a given newssource within K2 days.

Selection Bias (Gatekeeping)

I Compute similarity among news sources using the Jaccardcoefficient.

I Project it in two dimensions using PCA.

Selection Bias (Gatekeeping)

News Sources

Geographical pattern

Twitter

No clear pattern.

Coverage

I Compute similarity among news sources using theJensen-Shannon divergence (JS) .

Coverage Bias(s1, s2) = 1− JS(s1, s2)

Coverage Bias

News Sources

Stronger geographical pattern.

Coverage Bias

Twitter

Geographical pattern.

Political leaning

News Sources Twitter

Stronger political leaning signal in Twitter.

Statement

I Use sentiment analyses to find positive/negativesentiments associated to a person.

Statement

Obama Thatcher

Sentiments are more extreme in Twitter.

Conclusions

I Strong geographical patterns.I Political leaning signal is stronger in Social Media.I Feelings are more extreme in Social Media.