Muhammad Fareed Zaffar

Nouar Aldahoul, Hazem Ibrahim, Majd Mahmutoglu et al.

Schadenfreude, or the pleasure derived from others' misfortunes, has become a visible and performative feature of online news engagement, yet little is known about its prevalence, dynamics, or social patterning. We examine schadenfreude on Facebook over a ten-year period across nine major news publishers in the United States, the United Kingdom, and India (one left-leaning, one right-leaning, and one centrist per country). Using a combination of human annotation and machine-learning classification, we identify posts describing misfortune and detect schadenfreude in nearly one million associated comments. We find that while sadness and anger dominate reactions to misfortune posts, laughter and amusement form a substantial and patterned minority. Schadenfreude is most frequent in moralized and political contexts, higher among right-leaning audiences, and more pronounced in India than in the United States or United Kingdom. Temporal and regression analyses further reveal asymmetric relationships between political power and schadenfreude: left-leaning outlets display "power-licensed" schadenfreude that increases when their party governs, while right-leaning outlets exhibit "power-compensatory" schadenfreude that intensifies in opposition. Together, our findings move beyond anecdotal accounts to map schadenfreude as a dynamic, context-dependent feature of digital discourse, revealing how it evolves over time and across ideological and cultural divides.

Hazem Ibrahim, Nouar AlDahoul, Syed Mustafa Ali Abbasi et al.

The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum.

Muhammad Haroon, Fareed Zaffar, Padmini Srinivasan et al.

Stylometric approaches have been shown to be quite effective for real-world authorship attribution. To mitigate the privacy threat posed by authorship attribution, researchers have proposed automated authorship obfuscation approaches that aim to conceal the stylometric artefacts that give away the identity of an anonymous document's author. Recent work has focused on authorship obfuscation approaches that rely on black-box access to an attribution classifier to evade attribution while preserving semantics. However, to be useful under a realistic threat model, it is important that these obfuscation approaches work well even when the adversary's attribution classifier is different from the one used internally by the obfuscator. Unfortunately, existing authorship obfuscation approaches do not transfer well to unseen attribution classifiers. In this paper, we propose an ensemble-based approach for transferable authorship obfuscation. Our experiments show that if an obfuscator can evade an ensemble attribution classifier, which is based on multiple base attribution classifiers, it is more likely to transfer to different attribution classifiers. Our analysis shows that ensemble-based authorship obfuscation achieves better transferability because it combines the knowledge from each of the base attribution classifiers by essentially averaging their decision boundaries.