A Hierarchical Algorithm for Clustering Extremist Web Pages

Qi, Xingqin; Christensen, Kyle; Duval, Robert D.; Fuller, Edgar; Spahiu, Arian; Wu, Qin; Zhang, Cun-Quan

doi:10.1109/asonam.2010.81

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…One of the first computational frameworks, proposed by Bermingham et al [12] in 2009, combined social network analysis with sentiment detection tools to study the agenda of a radical YouTube group: the authors examined the topics discussed within the group and their polarity, to model individuals' behavior and spot signs of extremism and intolerance, seemingly more prominent among female users. The detection of extremist content (on the Web) was also the focus of a 2010 work by Qi et al [49]. The authors applied hierarchical clustering to extremist Web pages to divide them into different pre-imposed categories (religious, anti immigration, etc.…”

Section: Related Workmentioning

confidence: 99%

Contagion dynamics of extremist propaganda in social networks

Ferrara

2017

Information Sciences

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Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamic activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.

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Section: Related Workmentioning

confidence: 99%

Contagion dynamics of extremist propaganda in social networks

Ferrara

2017

Information Sciences

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“…One of the first computational frameworks, proposed by Bermingham et al [29] in 2009, combined social network analysis with sentiment detection tools to study the agenda of a radical YouTube group: the authors examined the topics discussed within the group and their polarity, to model individuals' behavior and spot signs of extremism and intolerance, seemingly more prominent among female users. The detection of extremist content (on the Web) was also the focus of a 2010 work by Qi et al [30]. The authors applied hierarchical clustering to extremist Web pages to divide them into different categories (religious, politics, etc.…”

Section: Related Literaturementioning

confidence: 99%

Predicting Online Extremism, Content Adopters, and Interaction Reciprocity

Ferrara

Wang

Varol

et al. 2016

Lecture Notes in Computer Science

118

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Abstract-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 social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

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“…A graph or network is one of the most commonly used models to represent real-valued relationships of a set of input items. Since many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy, the graph-based clustering method for single dimensional datasets proposed in [24][25][26] can be extended as follows to directly cluster multidimensional datasets. Let = ( , ) be a graph with the vertex set and associated with weights:…”

Section: Multidimensional and Multimembership Clustering Methods For Smentioning

confidence: 99%

“…Especially in 2007, Ou and Zhang [24] proposed a new clustering method with the feature of hierarchical tree and overlapping clusters, the complexity of this method is (ℎ 2 log ) where ℎ denotes the height of the hierarchical structure. This method was, respectively, used to cluster extremist web pages [25] and some classic social networks [26] with single weighted edges.…”

Section: Related Workmentioning

confidence: 99%

A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

Zhao

Zhang

Wan

et al. 2013

Mathematical Problems in Engineering

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Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.

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A Hierarchical Algorithm for Clustering Extremist Web Pages

Cited by 13 publications

References 11 publications

Contagion dynamics of extremist propaganda in social networks

Contagion dynamics of extremist propaganda in social networks

Predicting Online Extremism, Content Adopters, and Interaction Reciprocity

A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

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