scholarly journals Spam Account Detection on Social Media using Machine Learning Methods: A Survey

2019 ◽  
Vol 7 (12) ◽  
pp. 230-233
Author(s):  
Jaladhi Pandya
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Social Media Analytics of the Internet of Things

2021 ◽  
Author(s):  
Jim Scheibmeir ◽  
Yashwant K. Malaiya
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Internet Of Things ◽  
Smart Cities ◽  
The Internet ◽  
Social Media Analytics ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.

scholarly journals Examination of ‘Interests’ and ‘Activities’ of Social Network users

2019 ◽  
Vol 9 (2) ◽  
pp. 2688-2693
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
General Population ◽  
Social Media Marketing ◽  
Family Status ◽  
Learning Methods ◽  
Attribute Data ◽  
Machine Learning Methods ◽  
The Social

The present study relates to the analysis of attribute data related to users of the social network VK. The general population N = 52,614 users is the intersection of audiences from two communities for social media marketing. Based on the collected statistics on the “interests” attribute, one can compile a generalized portrait of an IT specialist and online marketer: this is a man aged about 30 years old, not married, or who defines his family status as “everything is complicated”. He speaks an average of two languages, works for an organization, or studies at a university. He has about 370 followers on VK. The result based on the data from the field 'activities' is very close to the data from the field 'interests', and gives a similar picture of the generalized portrait of a specialist. As part of the study, the authors have learned how to segment users into the users that identify themselves as „IT specialists or online marketers‟, and „other‟ users, using machine learning methods

scholarly journals Deep Neural Networks Detect Suicide Risk from Textual Facebook Posts

2020 ◽  
Author(s):  
Yaakov Ophir ◽  
Refael Tikochinski ◽  
Christa Asterhan ◽  
Itay Sisso ◽  
Roi Reichart
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Suicide Risk ◽  
Mental Health Research ◽  
Learning Methods ◽  
Psychosocial Risks ◽  
Machine Learning Methods ◽  
Task Model ◽  
Wide Range ◽  
Objective Detection

Background: Detection of suicide risk is a highly prioritized, yet complicated task. In fact, five decades of suicide research produced predictions that were only marginally better than chance (AUCs = 0.56 – 0.58). Advanced machine learning methods open up new opportunities for progress in mental health research. In the present study, Artificial Neural Network (ANN) models were constructed to predict externally valid suicide risk from everyday language of social media users. Method: The dataset included 83,292 postings authored by 1,002 authenticated, active Facebook users, alongside clinically valid psychosocial information about the users. Results: Using Deep Contextualized Word Embeddings (CWEs) for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.606 ≤ AUC ≤ .608), the MTM produced improved prediction accuracy (.690 ≤ AUC ≤ .759), with substantially larger effect sizes (.701 ≤ d ≤ .994). Subsequent content analyses suggest that predictions did not rely on explicit suicide-related themes, but on a wide range of content. Conclusions: Advanced machine learning methods can improve our ability to predict suicide risk from everyday social media activities. The knowledge generated by this research may eventually lead to the development of more accurate and objective detection tools and get individuals the help they need in time.

scholarly journals Deep Neural Networks Detect Suicide Risk from Textual Facebook Posts

2020 ◽  
Author(s):  
Yaakov Ophir ◽  
Refael Tikochinski ◽  
Christa Asterhan ◽  
Itay Sisso ◽  
Roi Reichart
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Suicide Risk ◽  
Mental Health Research ◽  
Learning Methods ◽  
Psychosocial Risks ◽  
Machine Learning Methods ◽  
Task Model ◽  
Wide Range ◽  
Objective Detection

Background: Detection of suicide risk is a highly prioritized, yet complicated task. In fact, five decades of suicide research produced predictions that were only marginally better than chance (AUCs = 0.56 – 0.58). Advanced machine learning methods open up new opportunities for progress in mental health research. In the present study, Artificial Neural Network (ANN) models were constructed to predict externally valid suicide risk from everyday language of social media users. Method: The dataset included 83,292 postings authored by 1,002 authenticated, active Facebook users, alongside clinically valid psychosocial information about the users. Results: Using Deep Contextualized Word Embeddings (CWEs) for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (.606 ≤ AUC ≤ .608), the MTM produced improved prediction accuracy (.690 ≤ AUC ≤ .759), with substantially larger effect sizes (.701 ≤ d ≤ .994). Subsequent content analyses suggest that predictions did not rely on explicit suicide-related themes, but on a wide range of content. Conclusions: Advanced machine learning methods can improve our ability to predict suicide risk from everyday social media activities. The knowledge generated by this research may eventually lead to the development of more accurate and objective detection tools and get individuals the help they need in time.

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