Clustering memes in social media

Abstract

Abstract-The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.

Average is Boring: How Similarity Kills a Meme's Success

“…In some works37 it can be a hashtag in Twitter. For this paper, we decide to focus on those memes whose template is a picture.…”

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...…”

Quality management architecture for social media data

“…Unsupervised learning approaches create a predictive model based on input data with a clustering method. Unsupervised approaches have been used for topical clustering of tweets [32,33], and tweet hashtag recommendations [34]. Supervised methods utilize training data (input/output about the phenomenon) for classification or regression type of algorithms [31].…”