Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, memberbased, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

This article investigates the problem of geosocial similarity among users of online social networks, based on the locations of their activities (e.g., posting messages or photographs). Finding pairs of geosocially similar users or detecting that two sets of locations (of activities) belong to the same user has important applications in privacy protection, recommendation systems, urban planning, and public health, among others. It is explained and shown empirically that common distance measures between sets of locations are inadequate for determining geosocial similarity. Two novel distance measures between sets of locations are introduced. One is the mutually nearest distance that is based on computing a matching between two sets. The second measure uses a quad-tree index. It is highly scalable but incurs the overhead of creating and maintaining the index. Algorithms with optimization techniques are developed for computing the two distance measures and also for finding the k -most-similar users of a given one. Extensive experiments, using geotagged messages from Twitter, show that the new distance measures are both more accurate and more efficient than existing ones.

Microblogs allow users to publish geo-tagged posts-short textual messages assigned to a geographic location. Users send posts from places they visit and discuss an idiosyncratic mixture of personal and general topics. Thus, it is reasonable to assume that the locations and the textual content of posts will be unique and will identify the posting user, to some extent. This raises the question whether there is a correlation between the locations of posts and their content. Are users who are similar from the geospatial perspective (i.e., who send messages from nearby locations) also similar from the textual perspective (i.e., send messages with similar textual content)? Do posts with similar content have a spatial distribution similar to that of any random set of posts? We present a study that focuses on these questions. We provide statistical tests to examine the correlation between textual content and geospatial locations in tweets. We show that although there is some correlation between locations and textual content, they provide different similarity measures, and combining these two properties for identification of users by their posts outperforms methods that merely use locations or only use the textual content, for identification.

No abstract

Although online communities have become popular both on the web and within enterprises, many of them often experience low levels of activity and engagement from their members. Previous studies identified the important role of community leaders in maintaining the health and vitality of their communities. One of their key means for doing so is by contributing relevant content to the community. In this paper, we study the effects of recommending social media content on enterprise community leaders. We conducted a large-scale user survey with four recommendation rounds, in which community leaders indicated their willingness to share social media items with their communities. They also had the option to instantly share these items. Recommendations were generated based on seven types of community interest profiles that were member-based, content-based, or hybrid. Our results attest that providing content recommendations to leaders can help uplift activity within their communities.