Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.

The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce ``word-of-mouth" effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual's influencing capability on its peers is by its reach for a certain action. We formulate identifying the influencers in a network as a problem of predicting the average depth of cascades an individual can trigger. We first empirically identify factors that play crucial role in triggering long cascades. Based on the analysis, we build a model for predicting the cascades triggered by a user for an action. The model uses features like influencing capabilities of the user and their friends, influencing capabilities of the particular action and other user and network characteristics. Experiments show that the model effectively improves the predictions over several baselines.

Many computational problems in game theory, such as finding Nash equilibria, are algorithmically hard to solve. This limitation forces analysts to limit attention to restricted subsets of the entire strategy space. We develop algorithms to identify rationally closed subsets of the strategy space under given size constraints. First, we modify an existing family of algorithms for rational closure in two-player games to compute a related rational closure concept, called formations , for n -player games. We then extend these algorithms to apply in cases where the utility function is partially specified, or there is a bound on the size of the restricted profile space. Finally, we evaluate the performance of these algorithms on a class of random games.