Advertisements (ads) are an innate part of search engine business models. To promote sales, advertisers are willing to pay search engines to promote their content to a prominent position in the search result page (SERP). This raises concerns about the search engine manipulation effect (SEME): the opinions of users can be influenced by the way search results are presented

Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions.

In recent years, enterprise group chat collaboration tools, such as Slack, IBM»s Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools - productivity increase and improved group communication - come significant challenges. Specifically, the 'always on' feature that makes it hard for users to cope with the load of conversational content and get up to speed after logging off for a while. In this demo, we present Collabot - a chat assistant service that implicitly learns users interests and social ties within a chat group and provides a personalized digest of missed content. Collabot assists users in coping with chat information overload by helping them understand the main topics discussed, collaborators, links and resources. This demo has two main contributions. First, we present a novel personalized group chat summarization algorithm; second the demonstration depicts a working implementation applied on different chat groups from different domains within IBM. A video, describing the demo can be found at https://www.youtube.com/watch?v=6cVsstiJ9vk.

In recent years, studies about trend detection in online social media streams have begun to emerge. Since not all users are likely to always be interested in the same set of trends, some of the research also focused on personalizing the trends by using some predefined personalized context.

In this paper, we present a crowdsourcing game platform that allows users to play, create, and share simple games that harness the collective intelligence of employees within the enterprise. The platform uses the wizard design pattern to guide users through the process of creating a game. We describe the platform in detail and report our findings from deploying it within a large global organization for a period of three months, in which 34 games were created by 25 employees and played by 339. We combine qualitative and quantitative analysis to understand the characteristics of the different games and their impact on popularity and engagement, to validate our design goals, and to suggest potential enhancements.

In binary-utility games, an agent can have only two possible utility values for final states, 1 (win) and 0 (lose). An adversarial binary-utility game is one where for each final state there must be at least one winning and one losing agent. We define an unbiased rational agent as one that seeks to maximize its utility value, but is equally likely to choose between states with the same utility value. This induces a probability distribution over the outcomes of the game, from which an agent can infer its probability to win. A single adversary binary game is one where there are only two possible outcomes, so that the winning probabilities remain binary values. In this case, the rational action for an agent is to play minimax. In this work we focus on the more complex, multiple-adversary environment. We propose a new algorithmic framework where agents try to maximize their winning probabilities. We begin by theoretically analyzing why an unbiased rational agent should take our approach in an unbounded environment and not that of the existing Paranoid or MaxN algorithms. We then expand our framework to a resource-bounded environment, where winning probabilities are estimated, and show empirical results supporting our claims.