scholarly journals Cyberattack detection model using community detection and text analysis on social media

ICT Express ◽  
2021 ◽  
Author(s):  
Jeong-Ha Park ◽  
Hyuk-Yoon Kwon
Keyword(s):  
Social Media ◽  
Community Detection ◽  
Text Analysis ◽  
Detection Model

scholarly journals Echo chamber detection and analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giacomo Villa ◽  
Gabriella Pasi ◽  
Marco Viviani
Keyword(s):  
Social Media ◽  
Community Detection ◽  
Virtual Communities ◽  
Selective Exposure ◽  
Social Needs ◽  
News Sources ◽  
Detection Strategy ◽  
Echo Chambers ◽  
Echo Chamber ◽  
Content Aware

AbstractSocial media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

scholarly journals Critical Discourse Analysis: Hegemony of the Social Media Twitter About National Issues in Indonesia and its Implications to the Discourse Analysis Subject in Colleges

2015 ◽  
Vol 2 (2) ◽  
pp. 153-166
Author(s):  
Elvi Susanti
Keyword(s):  
Social Media ◽  
Discourse Analysis ◽  
Critical Discourse Analysis ◽  
Text Analysis ◽  
Critical Discourse ◽  
The Social ◽  
Media Services ◽  
The World ◽  
Trending Topics ◽  
Discourse Practice

Abstract This research is linked with Twitter, as one of social media services on the Internet that are extremely popular in the world, including in Indonesia. This research is important because Twitter is effective in quickly and accurately delivering messages. In fact, everyone can act as a 'reporter' and form quick opinions through this social media. This research is aimed to investigate the emergence of the roots of hegemony based on text analysis that is linked with representation, relation, identity, and transformation of national issues that become trending topics on Twitter. Moreover, the research is to discuss the social media's discourse practice that influences media workers in producing news, and to see how it implicates the research on the study of discourse analysis. By using the Fairclough theory, especially on text analysis that is linked with representation, relation, and identity, the researcher attempts to explore how the roots of hegemony emerge in the national issues that become trending topics on Twitter. The researcher also offers a new function to complete the approach of Fairclough in text analysis on social media: transformation – which is an attempt to see the change in roles of news participants and amateur readers as 'reporters' and participate in forming opinions. Abstrak Penelitian ini berhubungan dengan twitter, sebagai salah satu media sosial di internet yang sangat populer di dunia, termasuk di indonesia. Penelitian ini penting karena twitter efektif dalam menyampaikan pesan dengan cepat dan akurat. Faktanya, semua orang dapat bertindak sebagai "reporter" dan membuat opini yang cepat melalui sosial media tersebut. Penelitian ini bertujuan untuk menyelidiki kemunculan dari akar hagemoni berdasarkan analisis teks yang berhubungan dengan representasi, hubungan, identitas, dan transformasi isu-isu nasional yang menjadi topik yang sedang tren di twitter. Selain itu, penelitian ini juga untuk mendiskusikan praktik wacana media sosial  yang mempengaruhi pekerja media dalam membuat berita, dan untuk melihat bagaimana hal tersebut melibatkan penelitian dalam studi analisis wacana. Dengan menggunakan teori Fairclough, khususnya pada analisis teks yang berhubungan dengan penafsiran, hubungan, identitas, peneliti berupaya untuk menyelidiki bagaimana akar hegemoni muncul yang menjadi topik tren di twitter. Peneliti juga menawarkan sebuah fungsi baru untuk melengkapi pendekatan Fairlclough dalam analisis teks pada sosial media: transformasi - yang merupakan usaha untuk melihat perubahan peran pembuat berita dan pembaca awam sebagai 'reporter' dan berpartisipasi dalam membentuk opini. How to Cite : Susanti, E. (2015). Hegemony of The Social Media Twitter About National Issues in Indonesia and Its Implications to the Discourse Analysis Subject in Colleges. TARBIYA: Journal Of Education In Muslim Society, 2(2), 153-166. doi:10.15408/tjems.v2i2.3180. Permalink/DOI: http://dx.doi.org/10.15408/tjems.v2i2.3180

Emotional talk about robotic technologies on Reddit: Sentiment analysis of life domains, motives, and temporal themes

2021 ◽  
pp. 146144482110672
Author(s):  
Nina Savela ◽  
David Garcia ◽  
Max Pellert ◽  
Atte Oksanen
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Text Analysis ◽  
Regression Models ◽  
Life Domains ◽  
Robotic Technology ◽  
Logistic Regression Models ◽  
Negative Comments ◽  
Media Platform ◽  
Robotic Technologies

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot ( N = 3,433,554), AI ( N = 2,821,614), automation ( N = 879,092), bot ( N = 21,559,939), intelligent agent ( N = 15,119), and software agent ( N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.

Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism

2022 ◽  
Vol 3 (1) ◽  
pp. 1-28
Author(s):  
Giorgio Grani ◽  
Andrea Lenzi ◽  
Paola Velardi
Keyword(s):  
Social Media ◽  
Data Extraction ◽  
Social Media Analytics ◽  
Text Compression ◽  
Emotional States ◽  
Agglomerative Clustering ◽  
Detection Model ◽  
Analytic Process ◽  
Personalized Health ◽  
Personalized Health Care

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization ( GTCACS ), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions ( ADRs ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.

Urban Region Function Mining Service Based on Social Media Text Analysis

2021 ◽  
Vol 31 (04) ◽  
pp. 563-586
Author(s):  
Yanchun Sun ◽  
Hang Yin ◽  
Jiu Wen ◽  
Zhiyu Sun
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Human Activities ◽  
Web Application ◽  
Classification Model ◽  
Urban Region ◽  
Urban Regions ◽  
Social Media Text ◽  
Urban Function ◽  
High Level

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions’ functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region’s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region’s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions’ function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

Network Text Analysis and Sentiment Analysis

2016 ◽  
pp. 137-153
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

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