Breaking the News: First Impressions Matter on Online News

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.Comment: The paper appears in ICWSM 201

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

Humanitarian Journalism

Humanitarian journalism can be defined, very broadly, as the production of factual accounts about crises and issues that affect human welfare. This can be broken down into two broad approaches: “traditional” reporting about humanitarian crises and issues, and advocacy journalism that aims to improve humanitarian outcomes. In practice, there is overlap between the two approaches. Mainstream journalists have long helped to raise awareness and funds for humanitarian crises, as well as provide early emergency warnings and monitor the treatment of citizens. Meanwhile, aid agencies and humanitarian campaigners frequently subsidize or directly provide journalistic content. There is a large research literature on humanitarian journalism. The most common focus of this research is the content of international reporting about humanitarian crises. These studies show that a small number of “high-profile” crises take up the vast majority of news coverage, leaving others marginalized and hidden. The quantity of coverage is not strongly correlated to the severity of a crisis or the number of people affected but, rather, its geopolitical significance and cultural proximity to the audience. Humanitarian journalism also tends to highlight international rescue efforts, fails to provide context about the causes of a crisis, and operates to erase the agency of local response teams and victims. Communication theorists have argued that this reporting prevents an empathetic and equal encounter between the audience and those affected by distant suffering. However, there are few empirical studies of the mechanisms through which news content influences audiences or policymakers. There are also very few production studies of the news organizations and journalists who produce humanitarian journalism. The research that does exist focuses heavily on news organizations based in the Global North/West

Humanitarian Journalism

Humanitarian journalism can be defined, very broadly, as the production of factual accounts about crises and issues that affect human welfare. This can be broken down into two broad approaches: “traditional” reporting about humanitarian crises and issues, and advocacy journalism that aims to improve humanitarian outcomes. In practice, there is overlap between the two approaches. Mainstream journalists have long helped to raise awareness and funds for humanitarian crises, as well as provide early emergency warnings and monitor the treatment of citizens. Meanwhile, aid agencies and humanitarian campaigners frequently subsidize or directly provide journalistic content. There is a large research literature on humanitarian journalism. The most common focus of this research is the content of international reporting about humanitarian crises. These studies show that a small number of “high-profile” crises take up the vast majority of news coverage, leaving others marginalized and hidden. The quantity of coverage is not strongly correlated to the severity of a crisis or the number of people affected but, rather, its geopolitical significance and cultural proximity to the audience. Humanitarian journalism also tends to highlight international rescue efforts, fails to provide context about the causes of a crisis, and operates to erase the agency of local response teams and victims. Communication theorists have argued that this reporting prevents an empathetic and equal encounter between the audience and those affected by distant suffering. However, there are few empirical studies of the mechanisms through which news content influences audiences or policymakers. There are also very few production studies of the news organizations and journalists who produce humanitarian journalism. The research that does exist focuses heavily on news organizations based in the Global North/West

Imaginary People Representing Real Numbers: Generating Personas from Online Social Media Data

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. We validate our approach by implementing the methodology into an actual working system; we then evaluate it via quantitative methods by examining the accuracy of predicting content preference of personas, the stability of the personas over time, and the generalizability of the method via applying to two other datasets. Research findings show the approach can develop rich personas representing the behavior and demographics of real audiences using privacy-preserving aggregated online social media data from major online platforms. Results have implications for media companies and other organizations distributing content via online platforms.</p

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people