Eventfully Safapp: hybrid approach to event detection for social media mining

Derbas, Nadia; Dusserre, Emmanuelle; Padró, Muntsa; Segond, Frédérique

doi:10.1007/s12652-018-1078-7

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…( 2018 ), Wang and Ye ( 2018 ), Alkouz and Al Aghbari ( 2020 ), Derbas et al. ( 2020 ) or generic types of events Rehman et al. ( 2020 ), but generally focus on extracting the main topic and subtopics of such events.…”

Section: Related Workmentioning

confidence: 99%

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

Afyouni

Khan

Alghbari

2022

J Ambient Intell Human Comput

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Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. We present ‘E-ware’ , a scalable and efficient big data platform that integrates data stream and geospatial processing tools for the incremental extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. Our incremental clustering technique employs temporal sliding windows, in order to update the discovered topic clusters with the upcoming social streams (i.e., tweets). The system integrates an efficient spatio-temporal index for fast retrieval and updates of evolving event clusters. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that E-ware has a major advantage for real-time incremental detection and tracking of events, both spatially and temporally. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

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Section: Related Workmentioning

confidence: 99%

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

Afyouni

Khan

Alghbari

2022

J Ambient Intell Human Comput

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“…For example, the counter-extremism interventions may want to identify the online content blaming the mainstream media platforms as biased or propaganda machines as this notion is one of the justifications radical groups tend to invite people to follow their channels on social media (Baugut & Neumann, 2020a). Derbas et al (2020) talked about SafApp, a technological tool that helps identify radical and extremism online particularly on social media platforms.…”

Section: Discussionmentioning

confidence: 99%

A Bibliometric Analysis of Radicalization through Social Media

Akram¹,

Nasar

2023

eab

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The purpose of this study is to synthesize the literature relating to radicalization on social media, a space with enhanced concerns about nurturing propaganda and conspiracies for violent extremism. Through the systematic review of 82 peer-reviewed studies related to radicalization through social media published in scholarly journals, this paper evidence the growth of robust studies on the usage of social media for radicalization. Nonetheless, the current work hardly discusses the radicalization issues through social media and reveals an increasing trend of publication from 2017 with a major contribution from the USA, Germany, and England. The thematic analysis indicated determinants of radicalization and the mitigation measures for the deradicalization of content on social media. However, the knowledge gap persists to understand the effects of radicalization in the different regional settings and further framing of content specific to target populations. Individuals must have the critical social media literacy to counteract the rising radicalization through social media. Individual users’ political interests are key factors in their radicalization such as citizens losing faith in the government and political parties. Active rather than passive searchers of violent radical material are more likely to engage in political violence. The results indicate that further research using experimental design, grounded theory, and pilot interventions may be relevant to suggest a solution to mitigate radicalization on social media.

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“…Discovering and disseminating events from diverse online social networks and with a variety of modes (e.g., text, image) have been the focus in many research studies, such as politics [ 1 ], traffic analysis [ 5 ], and fashion analysis [ 26 ]. Existing works on event detection aim at detecting specific [ 5 , 12 ] or generic types of events [ 22 ]. However, extracting other features that describe the evolution and spanning of such events over space and time need further investigation [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

2022

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Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised deep learning is used for feature extraction and topic classification, whereas unsupervised spatial clustering is employed to determine the event whereabouts. We present ‘Deep-Eware’, a scalable and efficient event-aware big data platform that integrates data stream and geospatial processing tools for the hybrid extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. The system integrates a semantic keyword generation tool using KeyBERT for dataset preparation. Event classification is performed using CNN and bidirectional LSTM, while hierarchical density-based spatial clustering was used for location-inference of events. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that this hybrid approach for spatio-temporal event extraction has a major advantage for real-time spatio-temporal event detection and tracking from social media. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

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Eventfully Safapp: hybrid approach to event detection for social media mining

Cited by 10 publications

References 7 publications

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs

A Bibliometric Analysis of Radicalization through Social Media

Deep-Eware: spatio-temporal social event detection using a hybrid learning model

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