scholarly journals Cyber Bullying Detection on Social Media using Machine Learning

2021 ◽  
pp. 410-416
Author(s):  
K. Mahesh ◽  
Suwarna Gothane ◽  
Aashish Toshniwal ◽  
Vinay Nagarale ◽  
Harish Gopu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Age Groups ◽  
Cyber Attacks ◽  
Cyber Bullying ◽  
Social Networking Websites ◽  
The Social ◽  
In The Beginning

From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought internet would be a host of numerous amazing services like the social networking. Today we can say that online applications and social networking websites have become a non-separable part of one’s life. Many people from diverse age groups spend hours daily on such websites. Despite the fact that people are emotionally connected together through social media, these facilities bring along big threats with them such as cyber-attacks, which includes cyberbullying.

scholarly journals Social Media Application for Specially Challenged

2021 ◽  
Author(s):  
Nishchal J
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Age Groups ◽  
Right To Information ◽  
Equal Right

Every person has an equal right to information, therefore, impairments shouldn’t restrict people from gaining this knowledge from any form of source. Social Networking applications have tremendously grown their popularity among all kinds of age groups for providing socialising opportunities, entertainment and exchange of knowledge. Hence, the motive of this paper is to propose a social networking application pipeline with a strong Machine Learning backend which makes it more accessible to the blind, deaf and dumb section of the society who otherwise do not enjoy the features of social networking platforms.

scholarly journals Social Media Application for Specially Challenged

2021 ◽  
Author(s):  
Nishchal J
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Age Groups ◽  
Right To Information ◽  
Equal Right

Every person has an equal right to information, therefore, impairments shouldn’t restrict people from gaining this knowledge from any form of source. Social Networking applications have tremendously grown their popularity among all kinds of age groups for providing socialising opportunities, entertainment and exchange of knowledge. Hence, the motive of this paper is to propose a social networking application pipeline with a strong Machine Learning backend which makes it more accessible to the blind, deaf and dumb section of the society who otherwise do not enjoy the features of social networking platforms.

scholarly journals A Machine Learning based Preventing the Occurrence of Cyber Bullying Messages on OSN

2019 ◽  
Vol 8 (2) ◽  
pp. 1861-1865
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Strategy ◽  
Cyber Bullying ◽  
The Internet ◽  
The Social

The process of threaten or harassment of any user with the help of posting wrong/abused or vulgar messages using the social media in the internet is known as Cyber bullying .These messages may sometime contain a text posted by a teen, or preteen or a child who want to threaten or harassed or embarrassed other child by posting the messages. So in this project, we mainly try to propose another depiction learning strategy to handle this issue known as SEMdae. Here the semantic augmentation comprises of predefined words that contain noise or abused meaning which is posted into the database by the admin and these words are classified based on the five categories that are available in the literature like “HATE, VULGAR, OFFENSIVE, SEX, and VOILENCE”.

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 Use of social networking websites as a care instrument for hospitalized adolescents

2017 ◽  
Vol 22 (1) ◽  
Author(s):  
Camila Amaral Borghi ◽  
Regina Szylit ◽  
Carolliny Rossi de Faria Ichikawa ◽  
Michelle Freire Baliza ◽  
Uyara Talmatare Jesus Camara ◽  
...  
Keyword(s):  
Social Support ◽  
Qualitative Study ◽  
Social Networking ◽  
Healthcare Professionals ◽  
Social Networking Websites ◽  
The Social ◽  
Hospital Resources ◽  
Social Networking Website ◽  
Important Form ◽  
Process Method

Abstract Objective: This study aimed to understand how social networking websites are used by adolescents and their importance during the hospitalization process. Method: A descriptive and qualitative study was supported by the virtual ethnographic method and resorted to the symbolic interactionism as theoretical framework. Eleven hospitalized adolescents were interviewed. Results: Three categories were identified based on the analysis of interviews and posts: Being able to use social networking websites during hospitalization; Using the Facebook® chat to keep connected to friends; Seeking support from friends through social networking websites. Final considerations: Facebook® was the social networking website that adolescents used the most, standing out as an important form of entertainment during hospitalization that facilitates communication and social support. Healthcare professionals should value the use of social networking websites by hospitalized adolescents and encourage access to these tools, providing hospital resources to expand and facilitate this access.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

scholarly journals Usage of Social Media by Leading Political Parties in General Election 2018 of Pakistan

2020 ◽  
Vol V (III) ◽  
pp. 32-43
Author(s):  
Ashraf Iqbal ◽  
Kishwer Perveen ◽  
Saima Waheed
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Political Parties ◽  
Social Networking ◽  
Political Party ◽  
Social Networking Sites ◽  
Dominant Party ◽  
The Social ◽  
Setting Theory ◽  
Social Media Tools

Social Networking sites are highly used for political proposes. In this study, the research tried to search the usage of social media by political parties during elections campaigns 2018 in Pakistan. The researcher applied the agenda-setting theory to link the social media posts of these political parties' pages and content analysis research technique for analyzing the variables. It was concluded from the that these social media are highly used for mobilizing voters where the users of these mediums not only see these posts but also like, comment and share for responding about what is uploaded on these social media pages by the representatives of political parties. It is concluded that from three trending political parties, PTI emerged as the most dominant party by using these social media tools, by uploading a maximum number of posts, by mobilizing voters to vote for a specific political party.

OPINI PUBLIK TERHADAP PENERAPAN NEW NORMAL DI MEDIA SOSIAL TWITTER

2020 ◽  
Vol 11 (1) ◽  
pp. 19-26
Author(s):  
AWAD BIN MUHAMMAD ALKATIRI ◽  
ZHAFIRA NADIAH ◽  
ADINDA NADA S. NASUTION
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Public Opinion ◽  
Thematic Analysis ◽  
Research Method ◽  
Age Groups ◽  
Validity And Reliability ◽  
New Normal ◽  
Quantitative Content ◽  
The Social

Social media is popular with all ages, people in young and old age groups can access social media. Social media is a place for information and opinion exchange. Twitter is one of the social media that is actively used in Indonesia. The new normal phenomenon that is currently being applied is wanted to be further known by researchers by referring to the hashtag #newnormalindonesia on Twitter. Researchers want to find out how public opinion is formed based on the hashtag #newnormalindonesia on Twitter. This research uses the concept of public opinion which is categorized into positive, negative, and neutral. In the research method, researchers use quantitative content analysis, the analysis unit uses thematic analysis units with the operationalization of concepts using the concept of public opinion. Coding sheets are used as instruments in data collection techniques, then in testing the validity and reliability using inter-coder reliability. The results showed that the twitter posts with the #newnormalindonesia hashtag tendto be negative by not supporting the implementation of new normal.

HuDA_COVID Human Disposition Analysis during COVID-19 using Machine Learning

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Health Status ◽  
Human Life ◽  
Age Groups ◽  
Psychological Needs ◽  
Mass Survey ◽  
Negative Effects ◽  
Methodical Approach ◽  
The Social ◽  
The Impact

Coronavirus has greatly impacted various aspects of human life, including human psychology & human disposition. In this paper, we attempted to analyze the impact of the COVID-19 pandemic on human health. We propose Human Disposition Analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied for human disposition analysis. A mass survey is conducted on individuals of various age groups, regions & professions, and the methodology achieved varied accuracy ranges of 87.5% to 98%. The study shows people are worried about lockdown, work & relationships. Furthermore, 23% of the respondents have not had any effect. 45% and 32% have had positive and negative effects, respectively. It is a novel study in human disposition analysis in COVID-19 where a novel weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.

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