Finding uncommon ground: Extremist online forum engagement predicts integrative complexity

Gregory, Andrew L; Piff, Paul K.

doi:10.1371/journal.pone.0245651

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Such spatial formations are found within the online world through online groups and communities that may contribute to group-think or ideological polarisation. Analysing scraped data from a white supremacist online forum, Stormfront.org, Gregory and Piff [ 56 ] found that both cognitive complexity and style matching decreased as engagement increased, indicating increased ideological polarisation during ongoing engagement rather than the deindividuation that characterises groupthink. Studying online video games, Robinson and Whittaker [ 57 ] argue that interactive gameplay and the use of iconography such as Nazi memorabilia creates conditions of belonging that can include adherence to extremist ideologies.…”

Section: Discussionmentioning

confidence: 99%

“…The literature both within this review and otherwise (see, e.g., [ 17 ]) identified social media, peer-to-peer, video hosting and collaborative platforms as conduits to extremist material. And there appears to be a growing number of radicalised individuals amongst younger users of short-attention video platforms, such as Tik Tok, gaming platforms, including Twitch, or online videogames [ 56 ]. Tik Tok, for example, is notable for its lack of enforcement around community guidelines and has been implicated in the rise of alt-right sentiment amongst young people [ 17 ] alongside Islamist content accessed by different young populations [ 57 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

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Public Mental Health Approaches to Online Radicalisation: An Empty Systematic Review

Mughal,

DeMarinis,

Nordendahl

et al. 2023

IJERPH

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This systematic review seeks to position online radicalisation within whole system frameworks incorporating individual, family, community and wider structural influences whilst reporting evidence of public mental health approaches for individuals engaging in radical online content. Methods: the authors searched Medline (via Ovid), PsycInfo (via Ebscohost) and Web of Science (Core Collection) with the use of Boolean operators across “extremism”, “online content” and “intervention”. Results: Following full-text assessments, all retrieved papers were excluded. No publications fulfilled the primary objective of reporting public mental health interventions specifically addressing online radicalisation. However, six publications fulfilled the secondary objective of identifying theoretical and conceptual relationships amongst elements in the three inclusion criteria (online extremism, psychological outcomes and intervention strategy) that could inform interventions within public mental health frameworks. These publications were quality assessed and discussed following the Cochrane Effective Practice and Organisation of Care guide for reporting empty reviews. Conclusions: there is an immediate need for further research in this field given the increase in different factions of radicalised beliefs resulting from online, particularly social media, usage.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Public Mental Health Approaches to Online Radicalisation: An Empty Systematic Review

Mughal,

DeMarinis,

Nordendahl

et al. 2023

IJERPH

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“…As of January 2023, a search of the keyword “web scraping” in Medline yielded 105 records, 95 of which were published starting from 2019. Articles using web scraping in health and social research mostly used information from social media [ 13 , 14 ] (eg, Twitter, Instagram, and TikTok), forums [ 15 , 16 ], business and review websites [ 17 ], and news web pages [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis

Galvez-Hernandez,

Gonzalez-Viana,

Gonzalez-de Paz

et al. 2024

JMIR Public Health Surveill

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Background Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. Objective We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. Methods This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults’ social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. Results We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. Conclusions The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables.

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What are the effects of different elements of media on radicalization outcomes? A systematic review

Wolfowicz

Hasisi

Weisburd

2022

Campbell Systematic Reviews

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Background Most national counter‐radicalization strategies identify the media, and particularly the Internet as key sources of risk for radicalization. However, the magnitude of the relationships between different types of media usage and radicalization remains unknown. Additionally, whether Internet‐related risk factors do indeed have greater impacts than other forms of media remain another unknown. Overall, despite extensive research of media effects in criminology, the relationship between media and radicalization has not been systematically investigated. Objectives This systematic review and meta‐analysis sought to (1) identify and synthesize the effects of different media‐related risk factors at the individual level, (2) identify the relative magnitudes of the effect sizes for the different risk factors, and (3) compare the effects between outcomes of cognitive and behavioral radicalization. The review also sought to examine sources of heterogeneity between different radicalizing ideologies. Search Methods Electronic searches were carried out in several relevant databases and inclusion decisions were guided by a published review protocol. In addition to these searches, leading researchers were contacted to try and identify unpublished or unidentified research. Hand searches of previously published reviews and research were also used to supplement the database searches. Searches were carried out until August 2020. Selection Criteria The review included quantitative studies that examined at least one media‐related risk factor (such as exposure to, or usage of a particular medium or mediated content) and its relationship to either cognitive or behavioral radicalization at the individual level. Data Collection and Analysis Random‐effects meta‐analysis was used for each risk factor individually and risk factors were arranged in rank‐order. Heterogeneity was explored using a combination of moderator analysis, meta‐regression, and sub‐group analysis. Results The review included 4 experimental and 49 observational studies. Most of the studies were judged to be of low quality and suffer from multiple, potential sources of bias. From the included studies, effect sizes pertaining to 23 media‐related risk factors were identified and analyzed for the outcome of cognitive radicalization, and two risk factors for the outcome of behavioral radicalization. Experimental evidence demonstrated that mere exposure to media theorized to increase cognitive radicalization was associated with a small increase in risk (g = 0.08, 95% confidence interval [CI] [−0.03, 19]). A slightly larger estimate was observed for those high in trait aggression (g = 0.13, 95% CI [0.01, 0.25]). Evidence from observational studies shows that for cognitive radicalization, risk factors such as television usage have no effect (r = 0.01, 95% CI [−0.06, 0.09]). However, passive (r = 0.24, 95% CI [0.18, 0.31]) and active (r = 0.22, 95% CI [0.15, 0.29]) forms of exposure to radical content online demonstrate small but potentially meaningful relationsh...

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Finding uncommon ground: Extremist online forum engagement predicts integrative complexity

Cited by 6 publications

References 40 publications

Public Mental Health Approaches to Online Radicalisation: An Empty Systematic Review

Public Mental Health Approaches to Online Radicalisation: An Empty Systematic Review

Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis

What are the effects of different elements of media on radicalization outcomes? A systematic review

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