Visual analytics in FCA-based clustering

Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

Detecting Real-World Influence Through Twitter

In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Swede

Academics’ online connections: Characterising the structure of personal networks on academic social networking sites and Twitter

Academic social networking sites (SNS), such as Academia.edu and ResearchGate, seek to bring the benefits of online social networking to academics' professional lives. Online academic social networking offers the potential to revolutionise academic publishing, foster novel collaborations, and empower academics to develop their professional identities online. However, the role that such sites play in relation to academic practice and other social media is not well understood at present. Arguably, the defining characteristic of academic social networking sites is the connections formed between profiles (in contrast to the traditional static academic homepage, for example). The social network of connections fostered by SNSs occupies an interesting space in relation to online identity, being both an attribute of an individual and shaped by the social context they are embedded within. As such, personal network structures may reflect an expression of identity (as "public displays of connection" (Donath & boyd, 2004) or "relational self portraits[s]" (Hogan & Wellman, 2014)), while social capital has been linked to network structures (Crossley et al., 2015). Network structure may therefore have implications for the types of roles that a network can play in professional life. What types of network structures are being fostered by academic SNS and how do they relate to academics' development of an online identity? This presentation will discuss findings from a project which has used a mixed-methods social network analysis approach to analyse academics' personal networks online. The personal networks of 55 academics (sampled from survey participants, to reflect a range of disciplines and job positions) on both one academic SNS (either Academia.edu or ResearchGate) and Twitter were collected and analysed. Differences in network structure emerged according to platform, with Twitter networks being larger and less dense, while academic SNS networks were smaller and more highly clustered. There were differences between academic SNS and Twitter in the brokerage positions occupied by the participant. The results are discussed in relation to other salient studies relating network structure in online social networks to social capital, and implications for academic practice. Future work, including co-interpretive interviews to explore the significance of network structures with participants, is introduced

On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore

On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore