Abdallah El Ali
Tim C. Stratmann
Souneil Park
Johannes Schöning
Wilko Heuten
Susanne C.J. Boll
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.
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A.chi2018_dataset.csv: Our final annotated, multi-language (English, Arabic, French, German) dataset (N=2,390). B.chi2018_news_queries.pdf: Contains a table that shows: (1) full set of queries (in English and native language) used to crawl Twitter user accounts (2) the total number of Twitter news accounts found (3) the amount and percentage found in each dataset (Beirut, Paris). C.chi2018_news_org_summary.csv: The final list of unique crawled news organizations (N=208). For ethical concerns, we only show the name, user name, the country / query used to retrieve the account, the follower count at the time of crawling, the number of tweets, and our mean and standard deviation scores of crowdsourced labels. D.chi2018_cf_instructions.pdf: Crowdsourcing annotation task instructions given to crowdworkers. Translated from English by native (bilingual) speakers into French, Arabic, and German. README.md
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Index Terms
- Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events
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