An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media

2016 ◽  
Vol 3 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Geetika Sarna ◽  
M.P.S. Bhatia
Keyword(s):  
Social Media ◽  
Preventive Measures ◽  
State Of The Art ◽  
Negative Information ◽  
Security And Privacy ◽  
Multimedia Content ◽  
The Real ◽  
The Social

Users on the social media can share positive as well as negative information intentionally and unintentionally in the form of multimedia content without knowing its impact on other user, which threatens the security and privacy of social media. Cyberbullying is one of the risks associated with social media. Cyberbullying is an aggressive act carried out intentionally against the victim by posting harmful material on social media to harm his/her reputation. Aggressive act creates depression, anxiety in users which may lead to diversion of attention and sometimes suicidal actions. In this paper the authors have included a survey on recent algorithms which work on detection of cyberbullying. State-of-the-art studies only focus on the detection of cyberbullying but not on the preventive measures against cyberbullying. In order to tackle this problem, the authors showed how the severity of the bullying in messages helps to find the real culprit.

An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media

2016 ◽  
pp. 160-181
Author(s):  
Geetika Sarna ◽  
M.P.S. Bhatia
Keyword(s):  
Social Media ◽  
Preventive Measures ◽  
State Of The Art ◽  
Negative Information ◽  
Security And Privacy ◽  
Multimedia Content ◽  
The Real ◽  
The Social

Users on the social media can share positive as well as negative information intentionally and unintentionally in the form of multimedia content without knowing its impact on other user, which threatens the security and privacy of social media. Cyberbullying is one of the risks associated with social media. Cyberbullying is an aggressive act carried out intentionally against the victim by posting harmful material on social media to harm his/her reputation. Aggressive act creates depression, anxiety in users which may lead to diversion of attention and sometimes suicidal actions. In this paper the authors have included a survey on recent algorithms which work on detection of cyberbullying. State-of-the-art studies only focus on the detection of cyberbullying but not on the preventive measures against cyberbullying. In order to tackle this problem, the authors showed how the severity of the bullying in messages helps to find the real culprit.

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

On the Privacy and Utility of Anonymized Social Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Yi Song ◽  
Xuesong Lu ◽  
Sadegh Nobari ◽  
Stéphane Bressan ◽  
Panagiotis Karras
Keyword(s):  
Social Networks ◽  
Social Media ◽  
State Of The Art ◽  
Social Groups ◽  
Trade Off ◽  
Private Lives ◽  
The Social ◽  
Before And After ◽  
Do So

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals LGBT dalam Perspektif Alkitab Sebagai Landasan Membentuk Paradigma Etika Kristen terhadap Pergaulan Orang Percaya

2020 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Christian Bayu Prakoso ◽  
Yonatan Alex Arifianto ◽  
Aji Suseno
Keyword(s):  
Social Media ◽  
Social Environment ◽  
Preventive Measures ◽  
The Other ◽  
Faith Formation ◽  
The Social ◽  
God's Love ◽  
Access Information ◽  
Lgbt People ◽  
The Church

The LGBT phenomenon is increasingly spreading among the wider community. The existence of social media allows everyone to access information quickly and easily. The church, which is directly related to the social environment, also takes an attitude towards this phenomenon. There are many different attitudes raised by a particular church or denomination. Therefore, this paper aims to find out carefully about the Bible's view of LGBT as the basis for forming a Christian ethical paradigm. The result of this research is that LGBT acts are a sin in God’s view. God does not want people to commit LGBT acts. But on the other hand, as an agent that embodies the application of God's love, the church is required to continue to follow LGBT people and provide faith formation and preventive measures to the congregation.

scholarly journals Towards Combating Pandemic-Related Misinformation in Social Media

2021 ◽  
pp. 140-158
Author(s):  
Isa Inuwa-Dutse
Keyword(s):  
Social Media ◽  
Public Discourse ◽  
Preventive Measures ◽  
State Of The Art ◽  
Fake News ◽  
Social Distancing ◽  
The Public ◽  
Toxic Impact ◽  
Online Social Media ◽  
Social Media Platforms

Conventional preventive measures during pandemics include social distancing and lockdown. Such measures in the time of social media brought about a new set of challenges – vulnerability to the toxic impact of online misinformation is high. A case in point is COVID-19. As the virus propagates, so does the associated misinformation and fake news about it leading to an infodemic. Since the outbreak, there has been a surge of studies investigating various aspects of the pandemic. Of interest to this chapter are studies centering on datasets from online social media platforms where the bulk of the public discourse happens. The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets; (2) offering relevant areas to study using the datasets; and (3) demonstrating how relevant datasets, strategies, and state-of-the-art IT tools can be leveraged in managing the pandemic.

scholarly journals PRESENTASI DIRI NETIZEN dalam KONSTRUKSI IDENTITAS di MEDIA SOSIAL dan KEHIDUPAN NYATA

2016 ◽  
Vol 15 (2) ◽  
pp. 103
Author(s):  
Nuriyatul Lailiyah
Keyword(s):  
Social Media ◽  
Real Life ◽  
Research Data ◽  
Positive Part ◽  
Self Presentation ◽  
The Real ◽  
Construction Of Identity ◽  
The Social ◽  
Media And Identity ◽  
Depth Interviews

In real life we often took identity as something given. Social media gave users the opportunity to present themselves as they wished. Social media gave chances to people to choose the kind of person they wished to be in social medai. People could then construct their identity the same as or different from their true selves in the real world.This study aimed to identify and understand the self-presentation of social media users in the construction of identity in social media and identity in real life. The study was conducted through the methods of phenomenology and avatar research. Data was gathered by by in-depth interviews and observations in informants social media accounts.The results showed several findings, namely: construction of identity in social media take the positive part of identity in the real world, informants consistently set a certain image in the social media in match to their expectations, social media became a mean of users personal branding. Informants also divided into two categories: first, the group that consistently maintain the image they were trying to build. second, groups that occasionally appeared different from the image they wanted to construct.

scholarly journals Who Are Controlled by The Same User? Multiple Identities Deception Detection via Social Interaction Activity (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13853-13854
Author(s):  
Jiacheng Li ◽  
Chunyuan Yuan ◽  
Wei Zhou ◽  
Jingli Wang ◽  
Songlin Hu
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Social Interactions ◽  
Real World ◽  
Deception Detection ◽  
State Of The Art ◽  
Multiple Identities ◽  
The Social ◽  
Real World Datasets ◽  
Multiple Accounts

Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.

SOCIAL MEDIA IMPERSONATION IN THE VIRTUAL WORLD

2021 ◽  
Vol 4 (1) ◽  
pp. 57-65
Author(s):  
Mohammed A Gharawi ◽  
Ahmed Badawy ◽  
Doaa Elsayed Ramadan ◽  
Shaymaa Elsayed
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Virtual World ◽  
Common Phenomenon ◽  
Comprehensive Review ◽  
Critical Part ◽  
The Real ◽  
Similar Profile ◽  
The Social ◽  
Social Media Platforms

Social media is becoming a critical part of everyone’s life. Social media has numerous platforms including Facebook, Twitter, Instagram, and LinkedIn. Impersonation is a common phenomenon found nearly on all social media platforms; it is the act of attempting to deceive someone by pretending that he is another person. Impersonators always try to hide a real account by making another similar profile to spread the fake contents on social media platforms making it very difficult to know the real accounts from the fake ones. Aims: This paper aims to write a comprehensive review on the social media impersonation, impersonation types, how to identify the social media impersonation, cases of social media impersonation, how to prevent impersonation, and how to protect the security of a social media user. Besides, the article explains the position of Islam toward impersonations including social media impersonation. Method: This is a narrative review of the existed literature review on the social media impersonation in the virtual world. Conclusion: Social media impersonation is the act of pretending that a person is another person which usually occurs on all social media platforms.

Structure-Based Analysis of Different Categories of Cyberbullying in Dynamic Social Network

2020 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Geetika Sarna ◽  
M. P. S. Bhatia
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Anomalous Behavior ◽  
The Other ◽  
Subtractive Clustering ◽  
Dynamic Social Network ◽  
Or Groups ◽  
The Social ◽  
Fuzzy C Means Clustering ◽  
Audio Video

Cyberbullying is the online fight between individuals or groups, and it can be viewed like harassment, rumor, denigration, exclusion, etc. Social networks are the main source of cyberbullying as various types of users interact with each other through text, audio, video, and images. One set of users uses the social media for the benefit of the whole society and the other set of users uses the social media for destructive purpose in the form of spreading rumors, harassment or to threaten others, etc., which is also called anomalous behavior. This article worked to detect the anomalous patterns using an exponential function and then proceeds to find the category of cyberbullying to which user belongs using subtractive clustering and fuzzy c-means clustering. The identification of category helps to find the extent to which these messages are harmful and based on which the culprit is apprehended or entrapped. State-of-the-art studies are focused on the detection of cyberbullying but this article captured different categories of cyberbullying.

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