Temporal analysis of radical dark web forum users

2016 ◽  
Author(s):  
Andrew J. Park ◽  
Brian Beck ◽  
Darrick Fletche ◽  
Patrick Lam ◽  
Herbert H. Tsang
Keyword(s):  
Temporal Analysis ◽  
Web Forum ◽  
Dark Web

Development and Application of Drug Abuse Ontology for the Analysis of substances of abuse on social media and dark web (Preprint)

2020 ◽  
Author(s):  
Usha Lokala
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Epidemiological Surveillance ◽  
Ontology Development ◽  
Web Based ◽  
Web Forum ◽  
Dark Web

BACKGROUND Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the utilization of these novel data sources for epidemiological surveillance of substance use behaviors and trends. OBJECTIVE The key aims are to describe development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance in the following domains: 1) user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S through analysis of Twitter and web forum data; and 3) trends in the availability of novel synthetic opioids through analysis of crypto market data. METHODS The domain and scope of the drug abuse ontology were defined using competency questions from two popular ontology methodologies (Neon and 101 ontology development methodology). The quality of the ontology is evaluated with a set of tools and best practices recognized by the Semantic Web community and the AI community that engage in natural language processing. The standard ontology metrics are also presented. The ontology was manually developed by the domain experts from the Center for Interventions, Treatment, and Addictions Research (CITAR) who used a range of data sources: 1) key epidemiological data sources and reports accessible through National Institute on Drug Abuse, Drug Enforcement Agency, European Monitoring Centre for Drugs Addiction, RxNorm and other; 2) prior peer-reviewed publications related to illicit opioids, cannabis, and other drugs; and 3) preliminary assessment and examination of web-based, social media sources related to selected substances. Sources of types 1 and 2 provided primary concepts while sources of type 3 were important in identifying alternative concepts including synonyms and street names. RESULTS The current version of Drug Abuse Ontology comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreases the false alarm rate by adding external knowledge to the learning process. The ontology is being updated to capture evolving concepts and has been used for three different projects: PREDOSE, eDrugTrends, and eDarkTrends. CONCLUSIONS It is found that the developed DAO is useful to identify the most frequently used terms/slang terms on social media/dark web related to drug abuse posted by the general population on social media and vendors on the dark web.

Drug Abuse Ontology for the study of Substance Use Epidemiology on Social Media and Dark Web: Ontology development and usability study (Preprint)

2020 ◽  
Author(s):  
Usha Lokala ◽  
Raminta Daniulaityte ◽  
Francois Lamy ◽  
Manas Gaur ◽  
Krishnaprasad Thirunarayan ◽  
...  
Keyword(s):  
Social Media ◽  
Substance Use ◽  
Drug Abuse ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Epidemiological Surveillance ◽  
Ontology Development ◽  
Web Based ◽  
Web Forum ◽  
Dark Web

BACKGROUND Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the utilization of these novel data sources for epidemiological surveillance of substance use behaviors and trends. OBJECTIVE The key aims are to describe the development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance for the following applications: 1) determining user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) understanding patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S through analysis of Twitter and web forum data; and 3) gleaning trends in the availability of novel synthetic opioids through analysis of crypto market data. METHODS The domain and scope of the drug abuse ontology were defined using competency questions from two popular ontology methodologies (Neon and 101 ontology development methodology). The quality of the ontology is evaluated with a set of tools and best practices recognized by the Semantic Web community and the AI community that engage in natural language processing. The standard ontology metrics are also presented. RESULTS The current version of Drug Abuse Ontology comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreases the false alarm rate by adding external knowledge to the learning process. The ontology is being updated to capture evolving concepts and has been used for four different projects: PREDOSE, eDrugTrends, eDarkTrends, DAO applications in Mental Health and COVID scenario. CONCLUSIONS It has been found that the developed Drug Abuse Ontology (DAO) is useful to identify the most frequently used terms/slang terms on social media/dark web related to drug abuse posted by the general population .

scholarly journals The Dark Web Forum Portal: From multi-lingual to video

2011 ◽  
Author(s):  
Hsinchun Chen ◽  
Dorothy Denning ◽  
Nancy Roberts ◽  
Catherine A. Larson ◽  
Ximing Yu ◽  
...  
Keyword(s):  
Web Forum ◽  
Dark Web

Twitter Usage Using Common Reference to Tinnitus

2020 ◽  
Vol 29 (2) ◽  
pp. 206-217
Author(s):  
Jianyuan Ni ◽  
Monica L. Bellon-Harn ◽  
Jiang Zhang ◽  
Yueqing Li ◽  
Vinaya Manchaiah
Keyword(s):  
Nonprofit Organizations ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Cross Sectional ◽  
For Profit ◽  
Common Reference ◽  
Personal Experiences ◽  
Twitter Users ◽  
Sectional Analysis ◽  
Share Information

Objective The objective of the study was to examine specific patterns of Twitter usage using common reference to tinnitus. Method The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results Around 70,000 tweets were identified and analyzed from May to October 2018. Of the 100 most active Twitter accounts, organizations owned 52%, individuals owned 44%, and 4% of the accounts were unknown. Commercial/for-profit and nonprofit organizations were the most common organization account owners (i.e., 26% and 16%, respectively). Seven unique tweets were identified with a reach of over 400 Twitter users. The greatest reach exceeded 2,000 users. Temporal analysis identified retweet outliers (> 200 retweets per hour) that corresponded to a widely publicized event involving the response of a Twitter user to another user's joke. Content analysis indicated that Twitter is a platform that primarily functions to advocate, share personal experiences, or share information about management of tinnitus rather than to provide social support and build relationships. Conclusions Twitter accounts owned by organizations outnumbered individual accounts, and commercial/for-profit user accounts were the most frequently active organization account type. Analyses of social media use can be helpful in discovering issues of interest to the tinnitus community as well as determining which users and organizations are dominating social network conversations.

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