Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Social media as intelligence in disaster response: eyewitness classification using community detection

Disasters cause widespread devastation to both physical infrastructure and the lives of individuals residing in large geographic areas. The disruption caused by disaster events is further compounded by high levels of uncertainty and information scarcity, presenting significant challenges to disaster response organisations and impeding the effectiveness of coordinated response efforts. The increasing use of digital technologies, such as social media, presents valuable sources of information that are available in real-time from geographically-distributed networks of ‘humans as sensors’. The data generated by these technologies can supplement traditional sources of intelligence to build models of situational awareness and inform decision-making, resulting in more effective disaster response operations. This thesis proposes a method of curating social media data to enhance its usefulness as a source of intelligence for disaster response organisations during crisis events. The research was conducted in four phases: (i) An ethnographic study developed a conceptual framework of the values and challenges of social media intelligence as perceived by disaster response practitioners. High data volume and low rates of relevance were established as key factors impeding integration with existing intelligence sources. (ii) Empirical studies of Twitter discourse were conducted during eight disaster events to identify patterns of online behaviour and establish the informative potential of social media data as a rich source of eyewitness reports. (iii) Geoproximate preferential attachment (homophily) was identified in the structure of Twitter relationship networks. An eyewitness classification model integrated relationship features for data curation. The model was evaluated on temporally-partitioned subgraphs and shown to be effective in real-time environments. (iv) The classification model was validated in simulated disaster response scenarios conducted with emergency service practitioners. Feedback from participants confirmed the effectiveness of the approach to improving the practical value of social media data as a source of intelligence during disaster response operations

TNE: a general time-aware network representation learning framework for temporal applications

Temporal dynamics such as short term and long term effects, recency effects, periodic and seasonal temporal factors in information networks are of great importance for many real-world applications. However, existing network embedding learning approaches mainly focus on semantic information or temporal phenomenon such as recency or dynamic process. They failed to have the capability of incorporating multiple temporal factors/phenomenon in information networks. To bridge the gap, this paper proposes a general time-aware network representation learning framework TNE for temporal applications. TNE contains a temporally annotated network TAN, a temporally annotated meta-path based random walk method, and a self-supervised embedding learning approach. We introduce temporal nodes and relations to existing information networks to construct TAN that can incorporate multiple temporal factors. We propose a temporally annotated meta-path based random walk approach to form a time-aware hybrid neighbourhood context that considers both semantic and temporal factors. Based on the time-aware context, self-supervised representation learning approaches are used to simultaneously preserve both semantic and temporal factors in embeddings. Extensive experiments of two large scale real-life datasets show that the proposed framework is effective in various temporal applications such as temporal similarity search and temporal recommendations