Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

TL;DR

This study analyzes Twitter coverage bias and sympathy towards Western and Arab media after the 2015 Beirut and Paris attacks, revealing regional differences in sympathy and their effects on information spread.

Contribution

It introduces a novel approach combining crowdsourced labels and deep learning to quantify and compare media sympathy bias on Twitter after crises.

Findings

Western media showed less sympathy than Arab media

Sympathetic tweets did not propagate further

Coverage was disproportionately skewed towards affected regions

Abstract

This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.