Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.

The use of tempting and often misleading headlines (clickbait) to allure readers has become a growing practice nowadays among the media outlets. The widespread use of clickbait risks the reader’s trust in media. In this paper, we present BaitBuster, a browser extension and social bot based framework, that detects clickbaits floating on the web, provides brief explanation behind its decision, and regularly makes users aware of potential clickbaits.

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.