Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?

Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter

“…Twitter bots who cannot be labeled as individuals or organizations may exist, but we expect they are rare. Further research should consider the correlation between our tool's predictions and the predictions made by systems such as BotOrNot or SentiBot (Dickerson et al, 2014). Future work could improve our tool by incorporating features used by these bot classifiers, though many such features cannot be computed when using only one tweet per user.…”

Sifting robotic from organic text: A natural language approach for detecting automation on Twitter

“…SentiBot, another content based classifier [19], utilizes latent Dirichlet allocation (LDA) for topical categorization combined with sentiment analysis techniques to classify individuals as either bots or humans. We note that as these automated entities evolve their strategies, combinations of our proposed methods and studies previously mentioned may be required to achieve reasonable standards for classification accuracy.…”

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 parti...…”