scholarly journals Metaheuristic Ant Lion and Moth Flame Optimization based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Cem Baydogan ◽  
Bilal Alatas
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Hate Speech ◽  
Automatic Detection ◽  
Novel Approach ◽  
Ant Lion

scholarly journals Misinformation, Radicalization and Hate Through the Lens of Users

2020 ◽  
Author(s):  
Manoel Horta Ribeiro ◽  
Virgílio A. F. Almeida ◽  
Wagner Meira Jr
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Network Structure ◽  
Online Social Networks ◽  
Hate Speech ◽  
Far Right ◽  
Content Creation ◽  
Political Opinion

The popularization of Online Social Networks has changed the dynamics of content creation and consumption. In this setting, society has witnessed an amplification in phenomena such as misinformation and hate speech. This dissertation studies these issues through the lens of users. In three case studies in social networks, we: (i) provide insight on how the perception of what is misinformation is altered by political opinion; (ii) propose a methodology to study hate speech on a user-level, showing that the network structure of users can improve the detection of the phenomenon; (iii) characterize user radicalization in far-right channels on YouTube through time, showing a growing migration towards the consumption of extreme content in the platform.

scholarly journals A ML and NLP based Framework for Sentiment Analysis on Bigdata

2020 ◽  
Vol 8 (5) ◽  
pp. 189-200
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Online Social Networks ◽  
Topic Model ◽  
Social Feedback ◽  
Feedback Systems ◽  
Multiple Sources ◽  
Probabilistic Topic Model ◽  
Novel Approach

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.

scholarly journals A Novel Approach to the Measurement of Bourdieusian Social Capital within Institutional Pages and Profiles

2022 ◽  
Vol 29 (1) ◽  
pp. 11-27
Author(s):  
Alan Keller Gomes ◽  
Kaique Matheus Rodrigues Cunha ◽  
Guilherme Augusto da Silva Ferreira
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Social Interactions ◽  
Online Social Networks ◽  
Mutual Recognition ◽  
Pierre Bourdieu ◽  
Sequential Patterns ◽  
Frequency Of Occurrence ◽  
Novel Approach ◽  
The Social

We present in this paper a novel approach for measuring Bourdieusian Social Capital (BSC) within  Institutional Pages and Profiles. We analyse Facebook's Institutional Pages and Twitter's Institutional Profiles. Supported by Pierre Bourdie's theory, we search for directions to identify and capture data related to sociability practices, i. e. actions performed such as Like, Comment and Share. The system of symbolic exchanges and mutual recognition treated by Pierre Bourdieu is represented and extracted automatically from these data in the form of generalized sequential patterns. In this format, the social interactions captured from each page are represented as sequences of actions. Next, we also use such data to measure the frequency of occurrence of each sequence. From such frequencies, we compute the effective mobilization capacity. Finally, the volume of BSC is computed based on the capacity of effective mobilization, the number of social interactions captured and the number of followers on each page. The results are aligned with Bourdieu's theory. The approach can be generalized to institutional pages or profiles in Online Social Networks.

Efficient Metaheuristic Approaches for Exploration of Online Social Networks

2013 ◽  
pp. 222-269 ◽  
Author(s):  
Zorica Stanimirović ◽  
Stefan Mišković
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Evolutionary Algorithm ◽  
Online Social Networks ◽  
Hybrid Methods ◽  
Optimization Techniques ◽  
Exact Methods ◽  
Novel Approach ◽  
Metaheuristic Methods ◽  
Efficient Exploration

This study presents a novel approach in analyzing big data from social networks based on optimization techniques for efficient exploration of information flow within a network. Three mathematical models are proposed, which use similar assumptions on a social network and different objective functions reflecting different search goals. Since social networks usually involve a large number of users, solving the proposed models to optimality is out of reach for exact methods due to memory or time limits. Therefore, three metaheuristic methods are designed to solve problems of large-scaled dimensions: a robust Evolutionary Algorithm and two hybrid methods that represent a combination of Evolutionary Algorithm with Local Search and Tabu Search methods, respectively. The results of computational experiments indicate that the proposed metaheuristic methods are efficient in detecting trends and linking behavior within a social network, which is important for providing a support to decision-making activities in a limited amount of time.

Efficient Metaheuristic Approaches for Exploration of Online Social Networks

Big Data ◽  
2016 ◽  
pp. 2098-2148
Author(s):  
Zorica Stanimirović ◽  
Stefan Mišković
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Evolutionary Algorithm ◽  
Online Social Networks ◽  
Hybrid Methods ◽  
Optimization Techniques ◽  
Exact Methods ◽  
Novel Approach ◽  
Metaheuristic Methods ◽  
Efficient Exploration

This study presents a novel approach in analyzing big data from social networks based on optimization techniques for efficient exploration of information flow within a network. Three mathematical models are proposed, which use similar assumptions on a social network and different objective functions reflecting different search goals. Since social networks usually involve a large number of users, solving the proposed models to optimality is out of reach for exact methods due to memory or time limits. Therefore, three metaheuristic methods are designed to solve problems of large-scaled dimensions: a robust Evolutionary Algorithm and two hybrid methods that represent a combination of Evolutionary Algorithm with Local Search and Tabu Search methods, respectively. The results of computational experiments indicate that the proposed metaheuristic methods are efficient in detecting trends and linking behavior within a social network, which is important for providing a support to decision-making activities in a limited amount of time.

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