Local Experts in Social Media

The problem of finding topic experts on social networking sites has been a continued topic of research. This thesis addresses the problem of identifying local experts in social media systems like Twitter. Local experts are experts with a topical expertise that is centered around a particular location. This geographically-constrained expertise can be a significant factor for enhanced answering of local information needs (What is the best pub in College Station?), for interacting with local experts (e.g., in the aftermath of a disaster), and for accessing local communities. I developed a local expert finding system – called OLE (online local experts) – that leverages the crowd sourced location-topic labels provided by users of the popular Twitter service. Concretely, I mine a collection of 108 million tweets for evidence of local topics of discussion occurring with user-mentions and location pairs; based on this collection, I developed a learning-to-rank approach that incorporates topic-location entropy and a local expert perimeter for varying the expertise focal window. In comparison with alternative expert finding approaches, I find that OLE is quite effective in finding local experts and achieves a 37.72% increase in mean average precision and a 16.8% increase in NDCG scores, across a comprehensive set of queries

To follow or not to follow? How Belgian health journalists use Twitter to monitor potential sources

Digital technology, the internet and mobile media are transforming the journalism and media landscape by influencing the news gathering and sourcing process. The empowering capacities of social media applications may constitute a key element for more balanced news access and “inclusive journalism”. We will build on two contrasting views that dominate the social media sourcing debate. On the one hand, literature shows that journalists of legacy media make use of social media sources to diversify their sourcing network including bottom-up sources such as ordinary citizens. On the other hand, various authors conclude that journalists stick with their old sourcing routines and continue to privilege top-down elite sources such as experts and government officials. In order to contribute to this academic debate we want to clarify the Twitter practices of professional Belgian health journalists in terms of how they use the platform to monitor potential sources. Therefore, we examined the 1146 Twitter “followings” of six Belgian health journalists by means of digital methods and social network analysis. Results show that top-down actors are overrepresented in the “following” networks and that Twitter’s “following” function is not used to reach out to bottom-up actors. In the overall network, we found that the health journalists mainly use Twitter as a “press club” (Rupar, 2015) to monitor media actors. If we zoom in specifically on the “following” network of the health-related actors, we found that media actors are still important, but experts become the most followed group. Our findings also underwrite the “power law” or “long tail” distribution of social network sites as very few actors take a central position in the “following” lists while the large majority of actors are not systematically monitored by the journalists

National Campaign to Reform State Juvenile Justice Systems and the National Communications Effort

Note: This evaluation is accompanied by an evaluation of the Models for Change initiative as well as introduction to the evaluation effort by MacArthur's President, Julia Stasch, and a response to the evaluation from the program team. Access these related materials here.This evaluation's principal focus was to determine if and how the National Communications Effort shaped and elevated a narrative that reached its target audiences and increased their understanding and support for juvenile justice reform. More broadly, the evaluation also examined the ways in which a complementary, experimental communications strategy could help support and sustain a movement of juvenile justice reform. Additionally, the MacArthur Foundation expressed a desire to know whether the National Communications Effort had an impact on policy reform and the broader media landscape, even though these were not objectives or intended outcomes of the National Communications Effort

Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano, Switzerlan

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Recruiting from the network: discovering Twitter users who can help combat Zika epidemics

Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to prominence in recent years as the cause of serious, long-lasting, population-wide health problems. In large countries like Brasil, traditional disease prevention programs led by health authorities have not been particularly effective. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement such efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives that are organised in local communities. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. We may then recommend these users as the prime candidates for direct engagement within their community. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results