407 research outputs found
Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events
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.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann,
T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring,
Understanding, and Classifying News Media Sympathy on Twitter after Crisis
Events. In Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems (CHI '18). ACM, New York, NY, USA. DOI:
https://doi.org/10.1145/3173574.317413
Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events
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.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann,
T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring,
Understanding, and Classifying News Media Sympathy on Twitter after Crisis
Events. In Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems (CHI '18). ACM, New York, NY, USA. DOI:
https://doi.org/10.1145/3173574.317413
Gradients of Fear and Anger in the Social Media Response to Terrorism
Research suggests that public fear and anger in wake of a terror attack can each uniquely contribute to policy attitudes and risk-avoidance behaviors. Given the importance of these negative-valanced emotions, there is value in studying how terror events can incite fear and anger at various times and locations relative to an attack. We analyze 36,259 Twitter posts authored in response to the 2016 Orlando nightclub shooting and examined how fear- and anger-related language varied with time and distance from the attack. Fear-related words sharply decreased over time, though the trend was strongest at locations near the attack, while anger-related words slightly decreased over time and increased with distance from Orlando. Comparing these results to usersâ pre-attack emotional language suggested that distant users remained both angry and fearful after the shooting, while users close to the attack remained angry but quickly reduced expressions of fear to pre-attack levels
"I've Tried so Hard to Make Good Americans Out of You": Legacy, Memory, and the Seattle General Strike of 1919
This historical project explores competing legacies and formation of memory within the Seattle General Strike of 1919 both in its after effects on the Seattle Labor Movement and the nation as a whole through the First Red Scare. This paper is divided into three chapters, an examination of the strike, national and local media coverage of the strike, and an examination of national and local repercussions from the strike. The Seattle General Strike of 1919 existed within an intersection of many disparate movementsâand truly has been memorialized as more than the sum of its parts. The Seattle General Strike has not been evaluated within the context of differing pro-capitalist and pro-worker solidarity viewpoints and how these two stories split, which this thesis will do
Are you Charlie or Ahmed? Cultural pluralism in Charlie Hebdo response on Twitter
We study the response to the Charlie Hebdo shootings of January 7, 2015 on
Twitter across the globe. We ask whether the stances on the issue of freedom of
speech can be modeled using established sociological theories, including
Huntington's culturalist Clash of Civilizations, and those taking into
consideration social context, including Density and Interdependence theories.
We find support for Huntington's culturalist explanation, in that the
established traditions and norms of one's "civilization" predetermine some of
one's opinion. However, at an individual level, we also find social context to
play a significant role, with non-Arabs living in Arab countries using
#JeSuisAhmed ("I am Ahmed") five times more often when they are embedded in a
mixed Arab/non-Arab (mention) network. Among Arabs living in the West, we find
a great variety of responses, not altogether associated with the size of their
expatriate community, suggesting other variables to be at play.Comment: International AAAI Conference on Web and Social Media (ICWSM), 201
- âŠ