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

Mapping Information Flows on Twitter

Social network services like Twitter and Facebook have created an expectation that you interact with your customers, followers and friends. There’s an expectation to connect rather than broadcast, listen and engage in conversations. But how can we expect to interact with our invisible audience when we can’t really see whose there? For the first time in history, there is a plethora of information produced by people’s actions. We can now observe a friend take another’s recommendation to purchase an item, or a powerful stream of clicks to content that we choose to curate. Social media professionals are jumping on the bandwagon and attempting to quantify social interactions by using terms like influence, reach, trust and klout. But even though data is more visible than ever, it is still representative of people’s complex reasoning mechanism, changing relationships, timing and logic. This paper looks at two different ways to analyze and display characteristics of online audiences on Twitter through information flows. By visualizing flows, it is possible to “put a face” to an audience, seeing interactions between interconnected users. By replaying a representation of the series of events, it is possible to note key moments in the act of information dissemination

Extracting Diurnal Patterns of Real World Activity from Social Media

In this study, we develop methods to identify verbal expressions in social media streams that refer to real-world activities. Using aggregate daily patterns of Foursquare checkins, our methods extract similar patterns from Twitter, extending the amount of available content while preserving high relevance. We devise and test several methods to extract such content, using time-series and semantic similarity. Evaluating on key activity categories available from Foursquare (coffee, food, shopping and nightlife), we show that our extraction methods are able to capture equivalent patterns in Twitter. By examining rudimentary categories of activity such as nightlife, food or shopping we peek at the fundamental rhythm of human behavior and observe when it is disrupted. We use data compiled during the abnormal conditions in New York City throughout Hurricane Sandy to examine the outcome of our methods

On the Study of Diurnal Urban Routines on Twitter

Social media activity in different geographic regions can expose a varied set of temporal patterns. We study and characterize diurnal patterns in social media data for different urban areas, with the goal of providing context and framing for reasoning about such patterns at different scales. Using one of the largest datasets to date of Twitter content associated with different locations, we examine within-day variability and across-day variability of diurnal keyword patterns for different locations. We show that only a few cities currently provide the magnitude of content needed to support such across-day variability analysis for more than a few keywords. Nevertheless, within-day diurnal variability can help in comparing activities and finding similarities between cities

The revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions

This article details the networked production and dissemination of news on Twitter during snapshots of the 2011 Tunisian and Egyptian Revolutions as seen through information flows—sets of near-duplicate tweets—across activists, bloggers, journalists, mainstream media outlets, and other engaged participants. We differentiate between these user types and analyze patterns of sourcing and routing information among them. We describe the symbiotic relationship between media outlets and individuals and the distinct roles particular user types appear to play. Using this analysis, we discuss how Twitter plays a key role in amplifying and spreading timely information across the globe