scholarly journals Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 539-543
Author(s):  
Sonali Gaikwad ◽  
Tejashri Borate ◽  
Nandpriya Ashtekar ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals A Survey on Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 430-433
Author(s):  
Prof. Priti Jorvekar ◽  
Sonali Gaikwad ◽  
Nandpriya Ashtekar ◽  
Tejashri Borate ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

10.2196/21660 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21660
Author(s):  
Tavleen Singh ◽  
Kirk Roberts ◽  
Trevor Cohen ◽  
Nathan Cobb ◽  
Jing Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Behavior ◽  
Health Behaviors ◽  
Peer Interactions ◽  
Social Media Platform ◽  
Risky Health Behaviors ◽  
Textual Data ◽  
Social Media Platforms ◽  
Media Platform

Background Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. Objective The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. Methods We performed a systematic review of the literature in September 2020 by searching three databases—PubMed, Web of Science, and Scopus—using relevant keywords, such as “social media,” “online health communities,” “machine learning,” “data mining,” etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Conclusions Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

scholarly journals General Purpose Opinion Mining System for Social Media Platform using Machine Learning Based Api

2019 ◽  
Vol 8 (4) ◽  
pp. 9727-9732
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Opinion Mining ◽  
Research Work ◽  
General Purpose ◽  
Machine Learning Techniques ◽  
Mining System ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

With the growth of technology there is lot of data available on the internet. Social media platform like Twitter, FaceBook,Google+,whats app,instagram etc are the platform that allow people to share and express their views, ideas, thoughts and experiences about any topics, post messages across the world. There are mainly two types of textual information available on social media platforms. One is fact and another next one is sentiments or more formally it can also called opinion. The social media is a platform where people gives their opinion regularly. These opinions may contain some factual information. For the analysis of sentiments we required some tools. Mostly text based mining is used for opinion mining. Text mining required lots of different tools and research work. This paper, provides a machine learning techniques for opinion calculation in Twitter..

scholarly journals Online Hate Network Spreads Malicious COVID-19 Content Outside the Control of Individual Social Media Platforms

2020 ◽  
Author(s):  
Nicolas Velasquez ◽  
Rhys Leahy ◽  
Nicholas Johnson Restrepo ◽  
Yonatan Lupu ◽  
Richard Sear ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Social Media ◽  
Topic Analysis ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

Abstract We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

scholarly journals Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review (Preprint)

2020 ◽  
Author(s):  
Tavleen Singh ◽  
Kirk Roberts ◽  
Trevor Cohen ◽  
Nathan Cobb ◽  
Jing Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Behavior ◽  
Health Behaviors ◽  
Peer Interactions ◽  
Social Media Platform ◽  
Risky Health Behaviors ◽  
Textual Data ◽  
Social Media Platforms ◽  
Media Platform

BACKGROUND Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. OBJECTIVE The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. METHODS We performed a systematic review of the literature in September 2020 by searching three databases—PubMed, Web of Science, and Scopus—using relevant keywords, such as “social media,” “online health communities,” “machine learning,” “data mining,” etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. RESULTS The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. CONCLUSIONS Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

scholarly journals Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
N. Velásquez ◽  
R. Leahy ◽  
N. Johnson Restrepo ◽  
Y. Lupu ◽  
R. Sear ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Social Media ◽  
Topic Analysis ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

AbstractWe show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Habib Husein Jafar Al-Hadar’s Da’wa Content Commodification on Youtube

2021 ◽  
Vol 15 (1) ◽  
pp. 1-11
Author(s):  
Akhmad Roja Badrus Zaman ◽  
Mahin Muqaddam Assarwani
Keyword(s):  
Social Media ◽  
Young Person ◽  
Social Media Platform ◽  
Virtual Ethnography ◽  
The Social ◽  
People’S Perception ◽  
Social Media Platforms ◽  
Media Platform ◽  
Definition Of ◽  
Selection Of

Advances in technology and information provide new opportunities for preachers to be able to take part in spreading Islamic teachings through various social media platforms. One of the preachers who took the role to preach through social media was Habib Husein Jafar al-Hadar. This article examines Habib Husein Jafar’s missionary activities on the social media platform he uses, Youtube. The researcher analyzes the data by observing virtually and visually (virtual ethnography) on the da’wa content displayed by Habib Husein Jafar through Youtube. The study shows that: 1) the attention to the spiritual enlightenment efforts of the younger generation is the basis of the selection of the social media platform Youtube - because based on previous research, the users of this social media platform are 18-29 years of age; 2) starting from the da’wa consumers who are primarily young people, the content they present is suitable to their needs and lifestyle and 3) by using the concept of the circuit of culture analysis, Habib Husein Jafar in various ranges can reconstruct people’s perception of one’s definition of holiness. It is not limited based on normative appearance - cloaked and sacrificed, for example - but more on the substantive side, namely by behaving and having knowledgeable skills. With the variety of content, he could visualize himself as a pious young man by not abandoning his social status as a young person.

scholarly journals Are You on the Right Platform? A Conjoint Analysis of Social Media Preferences in Aesthetic Surgery Patients

2018 ◽  
Vol 39 (9) ◽  
pp. 1019-1032 ◽  
Author(s):  
Apoorve Nayyar ◽  
Jihane Jadi ◽  
Roja Garimella ◽  
Stephen Tyler Elkins-Williams ◽  
Kristalyn K Gallagher ◽  
...  
Keyword(s):  
Social Media ◽  
Conjoint Analysis ◽  
Aesthetic Surgery ◽  
Live Video ◽  
Social Media Platform ◽  
Comprehensive Information ◽  
Social Media Platforms ◽  
Media Platform ◽  
Delivery Mechanism ◽  
Media Preferences

Abstract Background Social media has become an indispensable tool for patients to learn about aesthetic surgery. Currently, procedure-specific patient preferences for social media platforms and content are unknown. Objectives The authors sought to evaluate social media preferences of patients seeking aesthetic surgery. Methods We utilized a choice-based conjoint analysis survey to analyze the preferences of patients seeking 3 common aesthetic procedures: breast augmentation (BA), facial rejuvenation (FR), and combined breast/abdominal surgery (BAB). Participants were asked to choose among social media platforms (Facebook, Twitter, Instagram, Snapchat, Pinterest, Tumblr, YouTube), information extent (basic, moderate, comprehensive), delivery mechanism (prerecorded video, live video, photographs, text description), messenger (surgeon, nurse/clinic staff, patient), and option for interactivity (yes/no). The survey was administered using an Internet crowdsourcing service (Amazon Mechanical Turk). Results A total of 647 participants were recruited: 201 in BA, 255 in FR, and 191 in BAB. Among attributes surveyed, participants in all 3 groups (BA, FR, BAB) valued social media platform as the most important (30.9%, 33.1%, 31.4%), followed by information extent (23.1%, 22.9%, 21.6%), delivery mechanism (18.9%, 17.4%, 18%), messenger (16%, 17%, 17.2%), and interactivity (11.1%, 9.8%, 11.8%). Within these attributes, Facebook ranked as the preferred platform, with comprehensive information extent, live video as the delivery mechanism, and surgeon as the messenger as most preferred. Conclusions The choice of social media platform is the most important factor for patients, and they indicated a preference for comprehensive information delivered by the surgeon via live video on Facebook. Our study elucidates social media usage in common aesthetic populations, which can help improve aesthetic patient outreach.

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