Twitter in Academic Conferences: Usage, Networking and Participation over Time

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members

Tracing and Predicting Collaboration for Junior Scholars

Academic publication is a key indicator for measuring scholars' scientific productivity and has a crucial impact on their future career. Previous work has identified the positive association between the number of collaborators and academic productivity, which motivates the problem of tracing and predicting potential collaborators for junior scholars. Nevertheless, the insufficient publication record makes current approaches less effective for junior scholars. In this paper, we present an exploratory study of predicting junior scholars' future co-authorship in three different network density. By combining features based on affiliation, geographic and content information, the proposed model significantly outperforms the baseline methods by 12% in terms of sensitivity. Furthermore, the experiment result shows the association between network density and feature selection strategy. Our study sheds light on the re-evaluation of existing approaches to connect scholars in the emerging worldwide Web of Scholars

Predicting Students Performance Based on Their Reading Behaviors

E-learning systems can support students in the on-line classroom environment by providing different learning materials. However, recent studies find that students may misuse such systems with a variety of strategies. One particular misused strategy, gaming the system, has repeatedly been found to negatively affect the students’ learning results. Unfortunately, methods to quantitatively capture such behavior are poorly developed, making it difficult to predict students learning outcomes. In this work, we tackle this problem based on a study of the 567,193 records of the 71 students’ reading behaviors from two classes in the academic year 2016. We first quantify the extent to which students misused the system and then predict their class performance based on the quantified results. Our results demonstrated that such misbehavior in the E-learning system can be quantified as a probability and then further used as a significant factor to predict students class learning outcomes with high accuracy

Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation

In this paper, we study the problem of Team Member Replacement: given a team of people embedded in a social network working on the same task, find a good candidate who can fit in the team after one team member becomes unavailable. We conjecture that a good team member replacement should have good skill matching as well as good structure matching. We formulate this problem using the concept of graph kernel. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall; and (b) scale sub-linearly.Comment: Initially submitted to KDD 201

The 5th International Workshop on Social Web for Disaster Management (SWDM'18): Collective Sensing, Trust, and Resilience in Global Crises

During large-scale emergencies such as natural and man-made disasters, a massive amount of information is posted by the public in social media. Collecting, aggregating, and presenting this information to stakeholders can be extremely challenging, particularly if an understanding of the “big picture” is sought. This international workshop, the fifth in the series, is a key venue for researchers and practitioners to discuss research challenges and technical issues around the usage of social media in disaster management