Under the Spotlight: Web Tracking in Indian Partisan News Websites

India is experiencing intense political partisanship and sectarian divisions. The paper performs, to the best of our knowledge, the first comprehensive analysis on the Indian online news media with respect to tracking and partisanship. We build a dataset of 103 online, mostly mainstream news websites. With the help of two experts, alongside data from the Media Ownership Monitor of the Reporters without Borders, we label these websites according to their partisanship (Left, Right, or Centre). We study and compare user tracking on these sites with different metrics: numbers of cookies, cookie synchronizations, device fingerprinting, and invisible pixel-based tracking. We find that Left and Centre websites serve more cookies than Right-leaning websites. However, through cookie synchronization, more user IDs are synchronized in Left websites than Right or Centre. Canvas fingerprinting is used similarly by Left and Right, and less by Centre. Invisible pixel-based tracking is 50% more intense in Centre-leaning websites than Right, and 25% more than Left. Desktop versions of news websites deliver more cookies than their mobile counterparts. A handful of third-parties are tracking users in most websites in this study. This paper, by demonstrating intense web tracking, has implications for research on overall privacy of users visiting partisan news websites in India

Developing an online hate classifier for multiple social media platforms

The proliferation of social media enables people to express their opinions widely online. However, at the same time, this has resulted in the emergence of conflict and hate, making online environments uninviting for users. Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. To address this research gap, we collect a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labeled as non-hateful and the remaining 20% labeled as hateful. We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). While all the models significantly outperform the keyword-based baseline classifier, XGBoost using all features performs the best (F1 = 0.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions. Findings support the generalizability of the best model, as the platform-specific results from Twitter and Wikipedia are comparable to their respective source papers. We make our code publicly available for application in real software systems as well as for further development by online hate researchers.</p

Drones and American Domestic Policy: An Analysis of Elite and Mass Opinion

Researchers have primarily focused on the use of drones for military purposes. Yet, understanding how the use of drones influences domestic policymaking from the perspective of mass and elite opinion was generally absent from the academic literature. The purpose of this descriptive study was to explore and describe the perceptions of policy elites and the mass public on the impact of drone use on domestic policy. Guided by Donohue, Tichenor and Olien\u27s theories of media framing and salience, mass opinion was measured through a convenience sample via the Walden Participant Pool, whereas elite opinion was measured through a purposive sampling design that targeted policy elites that are expects on drone policy, including academics and individuals working for the RAND Corporation, American Civil Liberties Union, the Federal Aviation Administration, and the Department of Defense. Sampling produced 108 respondents from the Participant Pool and 5 respondents from the elite survey. Data was analyzed descriptively using SPSS. Results suggested congruence in mass and elite opinion, particularly on the negative impact of drone use on privacy. These findings help advance the academic literature, by providing guidelines on the impact of drone use on domestic policymaking, particularly in the realm of privacy. The small sample size limited the inferences that could be drawn from the results. The study will lead to positive social change, by providing information on the potential impact of drones on society during their widespread adoption. Such data can be used by policymakers to generate rules that properly balance the technological value of drones in society with those rights that make a democratic society possible