A Semantic Graph-Based Approach for Radicalisation Detection on Social Media

Saif, Hassan; Dickinson, Thomas; Kastler, Leon; Fernández, Miriam; Alani, Harith

doi:10.1007/978-3-319-58068-5_35

Cited by 31 publications

(35 citation statements)

References 12 publications

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“…Hence, room for future work is to incorporate this information in our detection model, probably by using recurrent neural networks (RNN) [8] due to their ability to capture sequential information in text or by using Hierarchical Attention Network (HAN) [30] in order to allow the model to focus on key semantic concepts and entities. Another direction would be by moving from the back-of-concepts representation used in our model to the back-of-semantic-relations [25]. This can be done by extracting the semantic relations between named-entities in tweets (e.g., T sunami < location > Sumatra, Evacuation < place > HighP ark) and use them to learn a more effective semantic vector representation similarly.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

Burel

Saif

Alani

2017

Lecture Notes in Computer Science

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Abstract. When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models.

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Section: Discussion and Future Workmentioning

confidence: 99%

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

Burel

Saif

Alani

2017

Lecture Notes in Computer Science

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“…Regarding detection we can highlight the works of Berger [5,6], Agarwal [2], Ashcroft [3] and Saif [29].…”

Section: Computational Approachesmentioning

confidence: 99%

“…In 2017 Saif [29] proposed a semantic graph-based approach to identify pro vs. anti-ISIS social media accounts. The authors developed multiple classifiers and showed that, their proposed classifier, trained for semantic features, outperformed those trained from lexical, sentiment, topic and network features by 7.8% on average F1-measure.…”

Section: Computational Approachesmentioning

confidence: 99%

“…Request permissions from permissions@acm.org. WebSci '18, May [27][28][29][30]2018 A well known example is the so-called Islamic State (IS), which is arguably one of the leading organisations in the use of social media for sharing their propaganda, for raising funds, and for radicalising and recruiting individuals around the globe. According to a 2015 U.S government report, 3 this organisation succeeded in recruiting more than 25,000 foreign fighters in Syria and Iraq, including 4,500 from Europe and North America.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Roots of Radicalisation on Twitter

Fernández

Asif

Alani

2018

Proceedings of the 10th ACM Conference on Web Science

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In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. 1 Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of 'roots of radicalisation' from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 "general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation.

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“…KGs are a type of heterogeneous information network [3] where nodes represent entities and edges different types of relationships. Using knowledge from KGs has applications in many domains including information retrieval [4], radicalization detection [5], twitter analy-sis [6], recommendation [7], clustering [8], entity resolution [9], and generic exploratory search [10]. One common need for many classes of knowledge discovery tasks is that of explaining the relatedness between entities.…”

Section: Introductionmentioning

confidence: 99%

Building relatedness explanations from knowledge graphs

Pirrò

2019

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Knowledge graphs (KGs) are a key ingredient to complement search results, discover entities and their relations and support several knowledge discovery tasks. We face the problem of building relatedness explanations, that is, graphs that can explain how a pair of entities is related in a KG. Explanations can be used in a variety of tasks; from exploratory search to query answering. We formalize the notion of explanation and present two algorithms. The first, E4D (Explanations from Data), assembles explanations starting from all paths interlinking the source and target entity in the data. The second algorithm E4S (Explanations from Schema) builds explanations focused on a specific relatedness perspective expressed by providing a predicate. E4S first generates candidate explanation patterns at the level of schema; then, it assembles explanations by proceeding to their verification in the data. Given a set of paths, found by E4D or E4S, we describe different criteria to build explanations based on information-theory, diversity and their combination. As a concrete use-case of relatedness explanations, we introduce relatedness-based KG querying, which revisits the query-by-example paradigm from the perspective of relatedness explanations. We implemented all machineries in the RECAP tool, which is based on RDF and SPARQL. We discuss an evaluation of the explanation building algorithms and a comparison of RECAP with related systems on real-world data.

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A Semantic Graph-Based Approach for Radicalisation Detection on Social Media

Cited by 31 publications

References 12 publications

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

Understanding the Roots of Radicalisation on Twitter

Building relatedness explanations from knowledge graphs

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