• Corpus ID: 7745525

Finding Online Extremists in Social Networks

  title={Finding Online Extremists in Social Networks},
  author={Jytte Klausen and Christopher Marks and Tauhid Zaman},
Online extremists in social networks pose a new form of threat to the general public. These extremists range from cyberbullies who harass innocent users to terrorist organizations such as the Islamic State of Iraq and Syria (ISIS) that use social networks to recruit and incite violence. Currently social networks suspend the accounts of such extremists in response to user complaints. The challenge is that these extremist users simply create new accounts and continue their activities. In this… 

Detection of Violent Extremists in Social Media

An automatic detection scheme that using as little as three groups of information related to usernames, profile, and textual content of users, determines whether or not a given username belongs to an extremist user is designed.

Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts

  • H. AlvariP. Shakarian
  • Computer Science
    2019 2nd International Conference on Data Intelligence and Security (ICDIS)
  • 2019
This paper uses the well-known statistical technique "Hawkes Process" to quantify the influence of PSM accounts on the dissemination of malicious information on social media platforms and indicates that PSMs are significantly different from regular users in making a message viral.

Early Identification of Pathogenic Social Media Accounts

This paper proposes a time-decay causality metric and incorporates it into a causal community detection-based algorithm and demonstrates effectiveness and efficiency of the approach on a real-world dataset from Twitter.


It was shown, that low-dimensional psycholinguistic and semantic features of texts allow detecting extremist texts with quite good performance while lexical features allow recognizing topics of the detected extremist texts.

Prototype and Analytics for Discovery and Exploitation of Threat Networks on Social Media

The proposed prototype system fills a need in the intelligence community for a capability to automate manual construction and analysis of online threat networks, and incorporates several novel machine learning algorithms for threat actor discovery and characterization.

Jihadists on Social Media : A Critique of Data Collection Methodologies

A general model of data collection from social media, in the context of terrorism research, focusing on recent studies of jihadists is proposed, showing that the methods used are prone to sampling biases, and that the sampled datasets are not sufficiently filtered or validated to ensure reliability of conclusions derived from them.

Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media

A semi-supervised causal inference PSM detection framework, SemiPsm, is proposed to compensate for the lack of labeled data and leverages unlabeled data in the form of manifold regularization and only relies on cascade information.

Taming Social Bots: Detection, Exploration and Measurement

A comprehensive overview of the existing work from data mining and machine learning perspective is provided, relative strengths and weaknesses of various methods are discussed, make recommendations for researchers and practitioners, and propose novel directions for future research in taming the social bots.

Opinion Dynamics with Stubborn Agents

A discrete optimization formulation for the problem of maximally shifting opinions in a network by targeting nodes with stubborn agents is developed and it is shown that a small number of stubborn agents can non-trivially influence a large population using simulated networks.

Beyond Twitter Revolutions: The Impact of Digital Media Logistics on Terror Networks of Communication in Iraq and Syria from 2014 to 2016

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Automatic detection of cyber-recruitment by violent extremists

Methods for identifying the recruitment activities of violent groups within extremist social media websites and analysis indicates that automatic detection of online terrorist recruitment is a feasible task are presented.

Tweeting the Jihad: Social Media Networks of Western Foreign Fighters in Syria and Iraq

Social media have played an essential role in the jihadists’ operational strategy in Syria and Iraq, and beyond. Twitter in particular has been used to drive communications over other social media

New online ecology of adversarial aggregates: ISIS and beyond

An ecology evolving on a daily time scale that drives online support is uncovered, and a mathematical theory that describes it is provided, that predicts that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones.

Predicting online extremism, content adopters, and interaction reciprocity

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in

The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter

Presents a demographic snapshot of ISIS supporters on Twitter by analysing a sample of 20,000 ISIS-supporting Twitter accounts, mapping the locations, preferred languages, and the number and type of

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

An “air traffic control”-like dashboard is proposed, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints.

Learning to Classify Hate and Extremism Promoting Tweets

The problem of hate and extremism promoting tweet identification as a one-class classification problem is formulated and several linguistic features are proposed that are effective and demonstrate that the proposed approach is effective.

Modeling the Detection of Textual Cyberbullying

This work decomposes the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems and shows that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.

Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter

This paper presents the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media, and evaluates a wide range of features to investigate the effectiveness of automatic content polluter identification.

Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

This paper conducts a set of large-scale measurements with a collection of over 500,000 accounts and proposes a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg on Twitter.