Reconsidering online reputation systems

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm in whatever space they operate in. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs