Evaluation of extremist cohesion in a darknet forum using ERGM and LDA

Rashed, Mohammed Rafiuddin; Piorkowski, John; McCulloh, Ian

doi:10.1145/3341161.3343532

Cited by 4 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, Net Miner can interactively perform topic modeling and social network analysis. It is possible to visualize the topic modeling results expressed in keyword text as a 2-mode network through social network analysis ( Rashed et al, 2019 ). The Net Miner ver.…”

Section: Methodsmentioning

confidence: 99%

“…This analysis technique has been widely used has been widely used in academia to analyze text documents by applying text mining techniques ( Ramage et al, 2009 ). One of the topic modeling algorithms, latent Dirichlet allocation (LDA), calculates a specific number of topics by considering the probability distribution of terms related to the topic ( Rashed et al, 2019 ). Articles contain several topic-related terms that must be extracted and categorized from the appropriate text to explore the area of knowledge covered in the article.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

LDA-based topic modeling for COVID-19-related sports research trends

2022

View full text Add to dashboard Cite

IntroductionThe COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature.MethodsSports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term ‘COVID-19 sports’.ResultsAs a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. ‘Sports participation’, ‘elite players’, and ‘sports industry’ were macroscopically classified, and detailed research topics could be identified from each division.ConclusionThis study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

LDA-based topic modeling for COVID-19-related sports research trends

2022

View full text Add to dashboard Cite

show abstract

“…In another study, vector spaces were developed to ascertain the themes of Dark Web forums [33], while text mining and social network analysis (SNA) were utilized to discern themes within these forums [34]. The cohesion of topics predominantly discussed in each Dark Web forum was quantified using LDA [35], and similar forum patterns were detected using the hidden Markov model (HMM) and LDA [36]. Additionally, LDA has been applied to trend analysis on the Dark Web [37].…”

Section: B Topic Modelingmentioning

confidence: 99%

Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight

Shin,

Jang,

Kim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

“…Applying topic modeling to analyze discourse cohesion has been done in several works. Rashed et al [Rashed et al 2019] used the strategy to understand the behavior of extremist communities in the darknet. The paper identified that cohesion between members grows over time.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic

Menuzzo

Santanchè

Gomes-Jr

2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

View full text Add to dashboard Cite

Social media has been used as a method to alert and raise awareness among the population to help fight the COVID-19 pandemic. We argue that the discourse of municipalities and their respective mayors may have an influence on the behavior of the population and thus directly impact COVID-19 outcomes. This paper analyzes the diversity and cohesion of these discourses through posts published on Facebook, evaluating (i) diversity of topics discussed, (ii) topic evolution, and (iii) deviation from a central discourse. We also combine this information with epidemiological data to assess impact in the outcomes. In particular, we present two different Latent Dirichlet allocation (LDA) models to analyze how topics are being discussed by municipalities/mayors and compare how cohesion is related to the evolution of the pandemic. Our initial analysis suggests that municipalities tend to employ a unified discourse as a response to the worsening of epidemic outcomes. The results of our study could help to inform governments of better communication strategies in this and future health crisis.

show abstract

Evaluation of extremist cohesion in a darknet forum using ERGM and LDA

Cited by 4 publications

References 6 publications

LDA-based topic modeling for COVID-19-related sports research trends

LDA-based topic modeling for COVID-19-related sports research trends

Dark Side of the Web: Dark Web Classification Based on TextCNN and Topic Modeling Weight

Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic

Contact Info

Product

Resources

About