scholarly journals Design and Analysis of a Novel Authorship Verification Framework for Hijacked Social Media Accounts Compromised by a Human

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Suleyman Alterkavı ◽  
Hasan Erbay
Keyword(s):  
Social Media ◽  
Binary Classification ◽  
Online Social Network ◽  
Feature Weighting ◽  
Essential Step ◽  
Authorship Verification ◽  
Weight Method ◽  
Textual Features ◽  
Verification Model ◽  
Textual Feature

Compromising the online social network account of a genuine user, by imitating the user’s writing trait for malicious purposes, is a standard method. Then, when it happens, the fast and accurate detection of intruders is an essential step to control the damage. In other words, an efficient authorship verification model is a binary classification for the investigation of the text, whether it is written by a genuine user or not. Herein, a novel authorship verification framework for hijacked social media accounts, compromised by a human, is proposed. Significant textual features are derived from a Twitter-based dataset. They are composed of 16124 tweets with 280 characters crawled and manually annotated with the authorship information. XGBoost algorithm is then used to highlight the significance of each textual feature in the dataset. Furthermore, the ELECTRE approach is utilized for feature selection, and the rank exponent weight method is applied for feature weighting. The reduced dataset is evaluated with many classifiers, and the achieved result of the F-score is 94.4%.

scholarly journals Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

2021 ◽  
Vol 14 (1) ◽  
pp. 1-8
Author(s):  
Bagus Satria Wiguna ◽  
Cinthia Vairra Hudiyanti ◽  
Alqis Alqis Rausanfita ◽  
Agus Zainal Arifin
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Weighting Scheme ◽  
Individual Group ◽  
Social Media Platform ◽  
Polarity Score ◽  
Media Platform ◽  
Textual Features ◽  
Textual Feature

Twitter is a social media platform that is used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, in sentiment, there is an expression that is actually the opposite of what is mean to be, and this is called sarcasm. The existence of sarcasm in a Tweet is difficult to detect automatically by a system even by humans. In this research, we propose a weighting scheme based on inconsistency between sentimen of tweet contain in Indonesian and the usage of emoji. With the weighting scheme for the detection of sarcasm, it can be used to find out a sentiment about a event, topic, individual, group, or product's review. The proposed method is by calculating the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The experimental results of the model developed, obtained f1-score 87.5%, precision 90.5% and recall 84.8%. By using the textual features and emoji models, it can detect sarcasm in a Tweet.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

scholarly journals Combining textual features to detect cyberbullying in social media posts

2020 ◽  
Vol 176 ◽  
pp. 612-621
Author(s):  
Meisy Fortunatus ◽  
Patricia Anthony ◽  
Stuart Charters
Keyword(s):  
Social Media ◽  
Textual Features

scholarly journals Social Media Use and Innovations: Introduction to the Special Issue

2016 ◽  
Vol 3 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Petter Bae Brandtzaeg ◽  
Asbjørn Følstad
Keyword(s):  
Social Media ◽  
Social Network ◽  
Civic Engagement ◽  
Media Use ◽  
Online Social Network ◽  
Special Issue ◽  
Social Media Use

This special issue on "Social media use and innovations" of the Journal of Media Innovation provides an engaging view into innovative uses of social media as well as approaches for utilizing social media in innovation.  With three papers included, we cover experiences with an online social network for children (Stephanie Valentine and Tracy Hammond), design by youth for youth in projects on social media for civic engagement (Henry Mainsah, Petter Bae Brandtzaeg, and Asbjørn Følstad), and social platforms for corporate and community innovation (Marika Lüders).

A new approach for textual feature selection based on N-composite isolated labels

2019 ◽  
Vol 26 (2) ◽  
pp. 221-243
Author(s):  
Samir Elloumi
Keyword(s):  
Feature Selection ◽  
Formal Context ◽  
Formal Concept ◽  
Processing Efficiency ◽  
New Approach ◽  
Textual Data ◽  
Isolated Points ◽  
Document Categorization ◽  
Textual Features ◽  
Textual Feature

AbstractTextual Feature Selection (TFS) aims to extract relevant parts or segments from text as being the most relevant ones w.r.t. the information it expresses. The selected features are useful for automatic indexing, summarization, document categorization, knowledge discovery, so on. Regarding the huge amount of electronic textual data daily published, many challenges related to the semantic aspect as well as the processing efficiency are addressed. In this paper, we propose a new approach for TFS based on Formal Concept Analysis background. Mainly, we propose to extract textual features by exploring the regularities in a formal context where isolated points exist. We introduce the notion ofN-composite isolated points as a set ofNwords to be considered as a unique textual feature. We show that a reduced value ofN(between 1 and 3) allows extracting significant textual features compared with existing approaches even for non-completely covering an initial formal context.

A Novel Method to Dynamically Fix Threshold for Node Neighbourhood Based Link Prediction Techniques

2020 ◽  
Vol 15 (1) ◽  
pp. 17-34
Author(s):  
Anand Kumar Gupta ◽  
Neetu Sardana
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Binary Classification ◽  
Online Social Network ◽  
Threshold Value ◽  
Classification Problem ◽  
Similarity Score ◽  
The Social ◽  
Comparative Results ◽  
Prediction Techniques

The objective of an online social network is to amplify the stream of information among the users. This goal can be accomplished by maximizing interconnectivity among users using link prediction techniques. Existing link prediction techniques uses varied heuristics such as similarity score to predict possible connections. Link prediction can be considered a binary classification problem where probable class outcomes are presence and absence of connections. One of the challenges in classification is to decide threshold value. Since the social network is exceptionally dynamic in nature and each user possess different features, it is difficult to choose a static, common threshold which decides whether two non-connected users will form interconnectivity. This article proposes a novel technique, FIXT, that dynamically decides the threshold value for predicting the possibility of new link formation. The article evaluates the performance of FIXT with six baseline techniques. The comparative results depict that FIXT achieves accuracy up to 93% and outperforms baseline techniques.

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