Finding Opinion Manipulation Trolls in News Community Forums

@inproceedings{Mihaylov2015FindingOM,
  title={Finding Opinion Manipulation Trolls in News Community Forums},
  author={Todor Mihaylov and Georgi Georgiev and Preslav Nakov},
  booktitle={CoNLL},
  year={2015}
}
The emergence of user forums in electronic news media has given rise to the proliferation of opinion manipulation trolls. Finding such trolls automatically is a hard task, as there is no easy way to recognize or even to define what they are; this also makes it hard to get training and testing data. We solve this issue pragmatically: we assume that a user who is called a troll by several people is likely to be one. We experiment with different variations of this definition, and in each case we… 
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102 Citations
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References

SHOWING 1-10 OF 20 REFERENCES
Do Not Feel The Trolls
TLDR
The aim of this work is to use sentic computing, a new paradigm for the affective analysis of natural language text, to detect trolls and hence prevent web-users from being emotionally hurt by malicious posts.
Filtering Offensive Language in Online Communities using Grammatical Relations
TLDR
This paper analyzes the Offensive language in text messages posted in online communities, and proposes a new automatic sentence-level filtering approach that is able to semantically remove the offensive language by utilizing the grammatical relations among words.
Accurately detecting trolls in Slashdot Zoo via decluttering
TLDR
A general algorithm called TIA (short for Troll Identification Algorithm) to classify users of an online “signed” social network as malicious (e.g. trolls on Slashdot) or benign (i.e. normal honest users).
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
TLDR
This work develops a method for automatically distinguishing between positive and negative reviews and draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation.
Spatio-temporal grounding of claims made on the web
TLDR
This project note introduces the spatio-temporal challenges and planned semantic annotation activities that are part of the PHEME project.
Detecting Offensive Language in Social Media to Protect Adolescent Online Safety
  • Ying Chen, Yilu Zhou, Sencun Zhu, Heng Xu
  • Computer Science
    2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
  • 2012
TLDR
This work proposes the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media, and incorporates a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content.
Propagation of trust and distrust for the detection of trolls in a social network
Mining and summarizing customer reviews
TLDR
This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Machine learning in automated text categorization
TLDR
This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Adversarial Web Search
TLDR
It is shown that search engine spammers create false content and misleading links to lure unsuspecting visitors to pages filled with advertisements or malware, and work over the past decade or so that aims to discover such spamming activities is examined, demonstrating that this conflict is far from over.
...
...