The social media genome

Abstract

Abstract-Information propagation in social media depends not only on the static follower structure but also on the topicspecific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on information adoption and show that they differ significantly from the static follower network. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than 20% improvement, compared to purely structural features. We also demonstrate that knowledge of user genotypes and influence backbones allow for the design of effective strategies for latency minimization of topic-specific information spread.

Topical Alignment in Online Social Systems

“…Furthermore, they showed that, while some users create connections mostly based on friendship, others are more guided by the content that users produce and share. Bogdanov et al provide a model of pre-specified topics and verified the consistency of their use by Twitter users, they also applied this to predict influencers and to minimize the latency in information dissemination [4]. Meyers et al [9] were interested in how the rise of abrupt changes in the information flow dynamics influences the creation and removal of links.…”

“…We infer topics from clusters of highly associated hashtags in messages exchanged by users. This allows us to capture topics exposing latent higher-level semantic entities without the need of an external ontology or manual classification step [2,3,4].…”

“…We infer topics from clusters of highly associated hashtags in messages exchanged by users. This allows us to capture topics exposing latent higher-level semantic entities without the need of an external ontology or manual classification step [2,3,4]. affiliation to them indicates individual preferences in the wide range of topics available in the social network and it constitute our pool to assess similarity between users.…”

Data mining techniques in social media: A survey

“…A20 Classifying ecommerce information sharing behaviour by youths on social networking sites 2011 [38] A21 Clustering memes in social media 2013 [39] A22 Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation 2010 [40] A23 Collaborative visual modeling for automatic image annotation via sparse model coding 2012 [41] A24 Confucius and its intelligent disciples: integrating social with search 2010 [42] A25 Content Feature Enrichment for Analyzing Trust Relationships in Web Forums 2013 [43] A26 Content Matters : A study of hate groups detection based on social networks analysis and web mining 2013 [44] A27 Co-training over Domain-independent and Domain-dependent features for sentiment analysis of an online cancer support community 2013 [45] A28 Data-Mining Twitter and the Autism Spectrum Disorder : A Pilot Study 2014 [46] A29 Decision Fusion for Multimodal Biometrics Using Social Network Analysis 2014 [47] A30 Detecting Deception in Online Social Networks 2014 [48] A31 Enhancing financial performance with social media: An impression management perspective 2013 [49] A32 Enriching short text representation in microblog for clustering 2012 [50] A33 Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics 2011 [51] A34 A56 The potential of social media in delivering transport policy goals 2014 [74] A57 The social media genome: modeling individual topic-specific behavior in social media 2013 [75] A58 Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning 2014 [76] A59 Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media 2012 [77] A60 Unsupervised and supervised learning to evaluate event relatedness based on content mining from socialmedia streams 2012 [78] A61 Using explicit linguistic expressions of preference in social media to predict voting behavior 2013 [79] A62 Using inter-comment similarity for comment spam detection in Chinese blogs 2011 [80] A63 Using Sentiment to Detect Bots on Twitter: Are Humans more Opinionated than Bots? 2014 [81] A64 Using social media to enhance emergency situation awareness 2012 [82] A65 Web data extraction, applications and techniques: A survey 2014 [83] A66 What's in twitter: I know what part...…”

Cost-Effective Online Trending Topic Detection and Popularity Prediction in Microblogging

“…Some other greedy-based algorithm to get top-K influential users in social networks are proposed by [Du et al 2013;Wang et al 2010], and an algorithm is proposed by [Gomez-Rodriguez et al 2012] to infer website influence in blogs. In addition, topic-specific influence and backbone structures in networks are studied by [Bi et al 2014;Bogdanov et al 2013].…”