Volunteers have always been extremely crucial and in urgent need for nonprofit organizations (NPOs) to sustain their continuing operations. However, it is expensive and time-consuming to recruit volunteers using traditional approaches. In the Web 2.0 era, abundant and ubiquitous social media data opens a door to the possibility of automatic volunteer identification. In this article, we aim to fully explore this possibility by proposing a scheme that is able to predict users’ volunteerism tendency from user-generated contents collected from multiple social networks based on a conceptual volunteering decision model. We conducted comprehensive experiments to investigate the effectiveness of our proposed scheme and further discussed its generalizibility and extendability. This novel interdisciplinary research will potentially inspire more promising and important human-centered applications.

The vocabulary gap between health seekers and providers has hindered the cross-system operability and the inter-user reusability. To bridge this gap, this paper presents a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which are tightly linked and mutually reinforced. Local mining attempts to code the individual medical record by independently extracting the medical concepts from the medical record itself and then mapping them to authenticated terminologies. A corpus-aware terminology vocabulary is naturally constructed as a byproduct, which is used as the terminology space for global learning. Local mining approach, however, may suffer from information loss and lower precision, which are caused by the absence of key medical concepts and the presence of irrelevant medical concepts. Global learning, on the other hand, works towards enhancing the local medical coding via collaboratively discovering missing key terminologies and keeping off the irrelevant terminologies by analyzing the social neighbors. Comprehensive experiments well validate the proposed scheme and each of its component. Practically, this unsupervised scheme holds potential to large-scale data.

In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.

You are in a new city. You are not familiar with the places and neighborhoods. You want to know all about the exciting sights, food outlets, and cultural venues that the locals frequent, in particular those that suit your personal interests. Even though there exist many mapping, local search, and travel assistance sites, they mostly provide popular and famous listings such as Statue of Liberty and Eiffel Tower, which are well-known places but may not suit your personal needs or interests. Therefore, there is a gap between what tourists want and what dominant tourism resources are providing. In this work, we seek to provide a solution to bridge this gap by exploiting the rich user-generated location contents in location-based social networks in order to offer tourists the most relevant and personalized local venue recommendations. In particular, we first propose a novel Bayesian approach to extract the social dimensions of people at different geographical regions to capture their latent local interests. We next mine the local interest communities in each geographical region. We then represent each local community using aggregated behaviors of community members. Finally, we correlate communities across different regions and generate venue recommendations to tourists via cross-region community matching. We have sampled a representative subset of check-ins from Foursquare and experimentally verified the effectiveness of our proposed approaches.

Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data.

We often remember images and videos that we have seen or recorded before but cannot quite recall the exact venues or details of the contents. We typically have vague memories of the contents, which can often be expressed as a textual description and/or rough visual descriptions of the scenes. Using these vague memories, we then want to search for the corresponding videos of interest. We call this “Memory Recall based Video Search” (MRVS). To tackle this problem, we propose a video search system that permits a user to input his/her vague and incomplete query as a combination of text query, a sequence of visual queries, and/or concept queries. Here, a visual query is often in the form of a visual sketch depicting the outline of scenes within the desired video, while each corresponding concept query depicts a list of visual concepts that appears in that scene. As the query specified by users is generally approximate or incomplete, we need to develop techniques to handle this inexact and incomplete specification by also leveraging on user feedback to refine the specification. We utilize several innovative approaches to enhance the automatic search. First, we employ a visual query suggestion model to automatically suggest potential visual features to users as better queries. Second, we utilize a color similarity matrix to help compensate for inexact color specification in visual queries. Third, we leverage on the ordering of visual queries and/or concept queries to rerank the results by using a greedy algorithm. Moreover, as the query is inexact and there is likely to be only one or few possible answers, we incorporate an interactive feedback loop to permit the users to label related samples which are visually similar or semantically close to the relevant sample. Based on the labeled samples, we then propose optimization algorithms to update visual queries and concept weights to refine the search results. We conduct experiments on two large-scale video datasets: TRECVID 2010 and YouTube. The experimental results demonstrate that our proposed system is effective for MRVS tasks.

In location-based social networks (LBSNs), users implicitly interact with each other by visiting places, issuing comments and/or uploading photos. These heterogeneous interactions convey the latent information for identifying meaningful user groups, namely social communities, which exhibit unique location-oriented characteristics. In this work, we aim to detect and profile social communities in LBSNs by representing the heterogeneous interactions with a multimodality nonuniform hypergraph. Here, the vertices of the hypergraph are users, venues, textual comments or photos and the hyperedges characterize the k -partite heterogeneous interactions such as posting certain comments or uploading certain photos while visiting certain places. We then view each detected social community as a dense subgraph within the heterogeneous hypergraph, where the user community is constructed by the vertices and edges in the dense subgraph and the profile of the community is characterized by the vertices related with venues, comments and photos and their inter-relations. We present an efficient algorithm to detect the overlapped dense subgraphs, where the profile of each social community is guaranteed to be available by constraining the minimal number of vertices in each modality. Extensive experiments on Foursquare data well validated the effectiveness of the proposed framework in terms of detecting meaningful social communities and uncovering their underlying profiles in LBSNs.