scholarly journals Analysis of the social networking sites and the machine learning

2018 ◽  
Author(s):  
М Г Хачатрян ◽  
◽  
П И Чепик ◽  
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
The Social

scholarly journals The Perceived Impact of Social Networking Sites and Apps on the Social Capital of Saudi Postgraduate Students: A Case Study

2021 ◽  
Vol 13 (1) ◽  
pp. 20
Author(s):  
Abdulelah A. Alghamdi ◽  
Margaret Plunkett
Keyword(s):  
Social Capital ◽  
Life Satisfaction ◽  
Social Networking ◽  
Social Networking Sites ◽  
Civic Participation ◽  
Social Trust ◽  
Political Engagement ◽  
Postgraduate Students ◽  
The Social ◽  
The Impact

With the increased use of Social Networking Sites and Apps (SNSAs) in Saudi Arabia, it is important to consider the impact of this on the social lives of tertiary students, who are heavy users of such technology. A mixed methods study exploring the effect of SNSAs use on the social capital of Saudi postgraduate students was conducted using a multidimensional construct of social capital, which included the components of life satisfaction, social trust, civic participation, and political engagement. Data were collected through surveys and interviews involving 313 male and 293 female postgraduate students from Umm Al-Qura University (UQU) in Makkah. Findings show that male and female participants perceived SNSAs use impacting all components of social capital at a moderate and mainly positive level. Correlational analysis demonstrated medium to large positive correlations among components of social capital. Gender differences were not evident in the life satisfaction and social trust components; however, females reported more involvement with SNSAs for the purposes of political engagement while males reported more use for civic participation, which is an interesting finding, in light of the norms and traditional culture of Saudi society.

YouTube based religious hate speech and extremism detection dataset with machine learning baselines

2021 ◽  
pp. 1-9
Author(s):  
Noman Ashraf ◽  
Abid Rafiq ◽  
Sabur Butt ◽  
Hafiz Muhammad Faisal Shehzad ◽  
Grigori Sidorov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Nearest Neighbor ◽  
Hate Speech ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Speech Detection ◽  
Supervised Learning Algorithms ◽  
Youtube Videos

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.

Using Instagram as a Preparation Tool for Study Abroad

2020 ◽  
Vol 13 (2) ◽  
pp. 20-37
Author(s):  
Simon Park
Keyword(s):  
Study Abroad ◽  
Social Networking ◽  
Social Networking Sites ◽  
The United States ◽  
Language Teachers ◽  
Study Abroad Programs ◽  
Daily Lives ◽  
English Classes ◽  
The Social ◽  
Group Members

This paper describes the usage of Instagram (the social networking platform) in sophomore English classes at a private Japanese university. Instagram was used to help students prepare for their study abroad semester. Students created private Instagram accounts and used this platform for group exercises with a mixed group of students and staff at potential study abroad sites in the United States of America. The participants posted images and video of their daily lives and routines at their schools, and created posts based on tasks set by the instructor. Group members were then encouraged to ask each other questions and communicate through Instagram. The study found that Instagram usage has the potential to help students prepare linguistically and culturally for study abroad semesters. The paper recommends follow-up studies that use Instagram and other social networking sites to help students prepare culturally and linguistically for study abroad semesters. This has implications for language teachers who are teaching prospective study abroad students or are interested in incorporating technology into their classes, as well as coordinators of study abroad programs interested in modernizing their study abroad orientation programs. この論文では、日本の私立大学の 2 年生の英語クラスでの Instagram（SNS）の使 用法について説明します。 Instagram は、学生が留学の準備をするのを助けるため に使用されました。学生はプライベート Instagram アカウントを作成し、このプラ ットフォームを使用して、米国の潜在的な留学サイトで学生とスタッフの混合グル ープとのグループ演習を行いました。参加者は、学校での日常生活の画像や動画を 投稿し、講師が設定したタスクに基づいて投稿を作成しました。その後、グループ のメンバーはお互いに質問し、Instagram を介してコミュニケーションすることが 奨励されました。調査では、Instagram の使用により、学生が留学学期に向けて言 語的および文化的に準備するのに役立つ可能性があることがわかりました。この論 文では、Instagram やその他の SNS を使用して、学生が留学に向けて文化的および 言語的に準備するのに役立つ追跡調査を推奨しています。これは、将来の留学学生 を教えている、またはクラスにテクノロジーを組み込むことに関心のある語学教 師、および留学オリエンテーションプログラムの近代化に関心のある留学プログラ ムのコーディネーターに影響を及ぼします。

Segmenting the social networking sites users: an empirical study

2012 ◽  
Vol 7 (2) ◽  
pp. 136 ◽  
Author(s):  
Carlota Lorenzo Romero ◽  
Efthymios Constantinides ◽  
María del Carmen Alarcón del Amo
Keyword(s):  
Empirical Study ◽  
Social Networking ◽  
Social Networking Sites ◽  
The Social

scholarly journals Policy gaps and technological deficiencies in social networking environments: Implications for information sharing

2013 ◽  
Vol 15 (1) ◽  
Author(s):  
Stephen M. Mutula
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Business Education ◽  
Social Networking Sites ◽  
Internet Security ◽  
Literature Survey ◽  
Digital Networks ◽  
Education Politics ◽  
The Social ◽  
User Trust

Background: With the growing adoption and acceptance of social networking, there are increased concerns about the violation of the users’ legitimate rights such as privacy, confidentiality, trust, security, safety, content ownership, content accuracy, integrity, access and accessibility to computer and digital networks amongst others.Objectives: The study sought to investigate the following research objectives to: (1) describe the types of social networks, (2) examine global penetration of the social networks, (3) outline the users’ legitimate rights that must be protected in the social networking sites (SNS), (4) determine the methods employed by SNS to protect the users’ legitimate rights and (5) identify the policy gaps and technological deficiencies in the protection of the users’ legitimate rights in the SNS.Method: A literature survey and content analysis of the SNS user policies were used to address objective four and objective five respectively.Results: The most actively used sites were Facebook and Twitter. Asian markets were leading in participation and in creating content than any other region. Business, education, politics and governance sectors were actively using social networking sites. Social networking sites relied upon user trust and internet security features which however, were inefficient and inadequate.Conclusion: Whilst SNS were impacting people of varying ages and of various professional persuasions, there were increased concerns about the violation and infringement of the users’ legitimate rights. Reliance on user trust and technological security features SNS to protect the users’ legitimate rights seemed ineffectual and inadequate.

scholarly journals Use of Web 2.0 Social Networking Sites for Collaborative Sharing Research Information by the Social Science Research Scholars at Alagappa University, Karaikudi.

2020 ◽  
Vol 9 (3) ◽  
pp. 70-74
Keyword(s):  
Social Science ◽  
Web 2.0 ◽  
Social Networking ◽  
Social Networking Sites ◽  
Research Collaboration ◽  
Social Science Research ◽  
Science Research ◽  
Research Information ◽  
The Social ◽  
Strongly Agree

This study attempts to the Web 2.0 Social Networking Sites for Collaborative Sharing Research Information by the Social Science Research Scholars at Alagappa University, Karaikudi. A sample size 97 Scholars was selected by random sampling method. The data required for the study were collected through a questionnaire. The findings of the study: 30.9% of the respondents using Facebook/ WhatsApp along with most highly used in the popular web browser used for Google chrome 72.2% Google chrome. 48.5% of respondents’ preference of “Very Strongly Agree” Collaborate with Research projects and Teams. Whereas 46.4% “Research Collaboration “Strongly agree” of the respondents respectively. 30.9% purpose of Web 2.0 for Collaborations of Research Communication while 19.6% Opportunities and Learning for Web 2.0 tools support social interaction in the learning process of the respondents respectively.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

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