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

Studying social network sites with the combination of traditional social science and computational approaches

Social Network Sites (SNSs) are fundamentally changing the way humans connect, communicate and relate to one another and have attracted a considerable amount of research attention. In general, two distinct research approaches have been followed in the pursuit of results in this research area. First, established traditional social science methods, such as surveys and interviews, have been extensively used for inquiry-based research on SNSs. More recently, however, the advent of Application Programming Interfaces (APIs) has enabled data-centric approaches that have culminated in theory-free “big data” studies. Both of these approaches have advantages, disadvantages and limitations that need to be considered in SNS studies. The objective of this dissertation is to demonstrate how a suitable combination of these two approaches can lead to a better understanding of user behavior on SNSs and can enhance the design of such systems. To this end, I present two two-part studies that act as four pieces of evidence in support of this objective. In particular, these studies investigate whether a combination of survey and API-collected data can provide additional value and insights when a) predicting Facebook motivations, b) understanding social media selection, c) understanding patterns of communication on Facebook, and d) predicting and modeling tie strength, compared to what can be gained by following a traditional social science or a computational approach in isolation. I then discuss how the findings from these studies contribute to our understanding of online behavior both at the individual user level, e.g. how people navigate the SNS ecosystem, and at the level of dyadic relationships, e.g. how tie strength and interpersonal trust affect patterns of dyadic communication. Furthermore, I describe specific implications for SNS designers and researchers that arise from this work. For example, the work presented has theoretical implications for the Uses and Gratifications (U&G) framework and for the application of Rational Choice Theory (RCT) in the context of SNS interactions, and design implications such as enhancing SNS users’ privacy and convenience by supporting reciprocity of interactions. I also explain how the results of the conducted studies demonstrate the added value of combining traditional social science and computational methods for the study of SNSs, and, finally, I provide reflections on the strengths and limitations of the overall research approach that can be of use to similar research efforts.As Redes Sociais (SNSs - Social Network Sites) estão a mudar de form fundamental a maneira como os seres humanos estabelecem ligações entre si, como comunicam e como relacionam-se uns com os outros, tendo atraído uma considerável quantidade de atenção investigativa. Em geral, duas abordagens de investigação distintas foram seguidas na procura de resultados nesta área de investigação. Em primeiro lugar, os já estabelecidos métodos tradicionais das ciências sociais, tais como inquéritos e entrevistas foram amplamente utilizados na investigação baseada em SNSs. Contudo, o surgimento mais recente das Interfaces de Programação de Aplicações (APIs - Application Programming Interfaces) tem permitido abordagens centradas em dados que têm culminado em estudos de "dados extensos", livres de teoria. Ambas estas abordagens têm vantagens, desvantagens e limitações que precisam de ser consideradas nos estudos de SNS. O objectivo desta dissertação é demonstrar como uma combinação adequada destas duas abordagens pode levar a uma melhor compreensão do comportamento do utilizador em SNSs e pode melhorar a concepção de tais sistemas. Para esse efeito, apresento dois estudos, em duas partes, que funcionam como quatro peças de prova em apoio a este objectivo. Estes estudos investigam, em particular, se uma combinação de dados recolhidos através de inquéritos e API pode fornecer valor adicional e conhecimentos ao a) prever as motivações do Facebook, b) compreender a selecção dos meios de comunicação social, c) compreender os padrões de comunicação no Facebook, e d) prever e modelar a força dos laços, em comparação com o que pode ser ganho seguindo uma ciência social tradicional ou uma abordagem computacional isolada. Abordo em seguida como os resultados destes estudos contribuem para uma compreensão do comportamento online tanto a nível do utilizador individual, por exemplo, como as pessoas percorrem o ecossistema SNS, e ao nível das relações diádicas, por exemplo, como a força dos laços e a confiança interpessoal afectam os padrões de comunicação diádica. Além disso, descrevo as implicações específicas para os designers e investigadores do SNS que decorrem deste trabalho. Por exemplo, o trabalho apresentado tem implicações teóricas para o quadro de Usos e Gratificações (U&G - Uses and Gratifications framework) e para a aplicação da Teoria da Escolha Racional (RCT - Rational Choice Theory) no contexto das interacções SNS, e implicações de design, como o reforço da privacidade e conveniência dos utilizadores de SNS, com o apoio à reciprocidade das interacções. Explico também como os resultados dos estudos realizados demonstram o valor acrescentado de combinar as ciências sociais tradicionais e os métodos computacionais para o estudo de SNS, e, por fim, apresento reflexões sobre os pontos fortes e limitações da abordagem global de investigação que podem ser úteis a esforços de investigação semelhantes

CommuNety: deep learning-based face recognition system for the prediction of cohesive communities

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities

Online Social Network Friends and Spatio-temporal Proximity of Their Geotagged Photos – A Case Study of Flickr Data

This empirical study aims to analyze relationships between online social network (OSN) friends and spatio-temporal proximity of their geotagged photos, using Flickr data as a case study. First, this study analyzes whether Flickr friends tend to post geotagged photos that are closer to each other compared to Flickr non-friends in space and time. Second, this study investigates whether the number of geotagged photos posted by users is related to the distance and time difference between their geotagged photos. Third, this study examines the spatial distributions of geotagged photos of Flickr friends within specific distance intervals to further understand the geographic meanings of Flickr user’s geotagging activities. Findings of this study can improve our understanding of the relationship between users’ virtual friendships and their physical activities. These understandings can support future research, including location-based services, location-based OSN searches, and location-based online marketing

Mining the Cloud of Witness: Inferring the Prestige of Saints from Medieval Paintings

This paper explores the possibility of applying the concept of distant reading to visual culture Using a corpus of medieval images of St Francis the study incorporates data mining and network analysis to infer the prestige of saints In turn these measurements provide the basis for testing and exploring themes related to the artwork Despite some methodological issues the approach produces results consistent with our historical understanding of this artwork and its contex

Supporting meaningful social networks

Recent years have seen exponential growth of social network sites (SNSs) such as Friendster, MySpace and Facebook. SNSs flatten the real-world social network by making personal information and social structure visible to users outside the ego-centric networks. They provide a new basis of trust and credibility upon the Internet and Web infrastructure for users to communicate and share information. For the vast majority of social networks, it takes only a few clicks to befriend other members. People’s dynamic ever-changing real-world connections are translated to static links which, once formed, are permanent – thus entailing zero maintenance. The existence of static links as public exhibition of private connections causes the problem of friendship inflation, which refers to the online practice that users will usually acquire much more “friends” on SNSs than they can actually maintain in the real world. There is mounting evidence both in social science and statistical analysis to support the idea that there has been an inflated number of digital friendship connections on most SNSs. The theory of friendship inflation is also evidenced by our nearly 3-year observation on Facebook users in the University of Southampton. Friendship inflation can devalue the social graph and eventually lead to the decline of a social network site. From Sixdegrees.com to Facebook.com, there have been rise and fall of many social networks. We argue that friendship inflation is one of the main forces driving this move. Despite the gravity of the issue, there is surprisingly little academic research carried out to address the problems. The thesis proposes a novel algorithm, called ActiveLink, to identify meaningful online social connections. The innovation of the algorithm lies in the combination of preferential attachment and assortativity. The algorithm can identify long-range connections which may not be captured by simple reciprocity algorithms. We have tested the key ideas of the algorithms on the data set of 22,553 Facebook users in the network of University of Southampton. To better support the development of SNSs, we discuss an SNS model called RealSpace, a social network architecture based on active links. The system introduces three other algorithms: social connectivity, proximity index and community structure detection. Finally, we look at the problems relating to improving the network model and social network systems

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure