2021
DOI: 10.31219/osf.io/57j8g
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Trends of Radicalization.D3.2 Country Report June 2021. Conducted under the Horizon 2020 project ‘De-Radicalisation in Europe and Beyond: Detect, Resolve, Re-integrate’ (959198).

Abstract: The aim of this report is to delineate trends of radicalization in Poland by evaluating specific ‘hotspots.’ The main source of data are the court files of twelve subjects, which are supplemented with academic literature, as well as offenders’ perspectives available in the legal documentation and their (social) media. The study provides rich qualitative evidence about how personal characteristics of the offenders, their direct environment, as well as systemic and structural factors that might have contributed … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 0 publications
0
2
0
Order By: Relevance
“…The growing tendencies of polarization and radicalization in Poland, which started around 2015 [6] and have proliferated on the Polish Internet [7][8][9], necessitated the need for more active studies on the language of polarization among Polish researchers. This resulted in a number of datasets and shared tasks being proposed.…”
Section: Background and Summarymentioning
confidence: 99%
See 1 more Smart Citation
“…The growing tendencies of polarization and radicalization in Poland, which started around 2015 [6] and have proliferated on the Polish Internet [7][8][9], necessitated the need for more active studies on the language of polarization among Polish researchers. This resulted in a number of datasets and shared tasks being proposed.…”
Section: Background and Summarymentioning
confidence: 99%
“…As an additional analysis, we compared the top thirty words from the harmful and non-harmful groups separately, extracted from the training dataset using standard term frequency with inverse document frequency (TF-IDF) 9 . Predictably, for the non-harmful group, none of the words with the highest TF-IDF were harmful, with the majority of them being related to soccer, due to this topic being prevalent in the discussions within the extracted messages.…”
Section: General Statistical Analysismentioning
confidence: 99%