scholarly journals When protests turn violent: The roles of moralization and moral convergence

2017 ◽  
Author(s):  
Marlon Mooijman ◽  
Joseph Hoover ◽  
Ying Lin ◽  
Heng Ji ◽  
Morteza Dehghani
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Behavioral Experiments ◽  
Moral Rhetoric ◽  
The Future ◽  
Using Data

We propose that the risk of violence at protests can be estimated as a function of individual moralization and perceived moral convergence. Using data from the 2015 Baltimore protests, we find that not only did the rate of moral rhetoric on social media increase on days with violent protests, but also that the hourly frequency of morally relevant tweets predicted the future rates of arrest during protests, suggesting an association between moralization and protest violence. To understand the structure of this association, we ran a series of controlled behavioral experiments demonstrating that people are more likely to endorse violent protest for a given issue when they moralize the issue; however, this effect is moderated by the degree people believe others share their values. We discuss how online social networks may contribute to inflations of protest violence.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

Is Social Media Marketing Really Working?

2013 ◽  
pp. 174-193 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

Social Media Analytics

2013 ◽  
pp. 212-228
Author(s):  
Sunil Kr Pandey ◽  
Vineet Kansal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Medical Science ◽  
Multimedia Data ◽  
Social Media Analytics ◽  
Multimedia Networks ◽  
Linkage Data ◽  
Insurance Sector ◽  
Tremendous Amount

Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and contain tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The growth of the usage and penetration of social media in the recent years has been enormous and unprecedented. This significant increase in its usage and increased number of users, there has been trend of a substantial increase in the volume of information generated by users of social media. Irrespective of primary domain in which organization is operating in to, whether it is insurance sector, social media (including facebook, twitter etc), medical science, banking etc. Virtually a large number of varying nature and services of organizations are making significant investments in social media. But it is also true that many are not systematically analyzing the valuable information that is resulting from their investments. This chapter aims at providing a data-centric view of online social networks; a topic which has been missing from much of the literature and to draw unanswered research issues which can be further explored to strengthen this area.

Is Social Media Marketing Really Working?

2014 ◽  
pp. 1260-1279 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

Towards a Unified Semantic Model for Online Social Networks to Ensure Interoperability and Aggregation for Analysis

2018 ◽  
pp. 978-1003
Author(s):  
Asmae El Kassiri ◽  
Fatima-Zahra Belouadha
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Semantic Model ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Knowing That ◽  
The Social ◽  
Industrial Projects ◽  
Media Organizations

The Online Social Networks (OSN) have a positive evolution due to the diversity of social media and the increase in the number of users. The revenue of the social media organizations is generated from the analysis of users' profiles and behaviors, knowing that surfers maintain several accounts on different OSNs. To satisfy its users, the social media organizations have initiated projects for ensuring interoperability to allow for users creating other accounts on other OSN using an initial account, and sharing content from one media to others. Believing that the future generations of Internet will be based on the semantic web technologies, multiple academic and industrial projects have emerged with the objective of modeling semantically the OSNs to ensure interoperability or data aggregation and analysis. In this chapter, we present related works and argue the necessity of a unified semantic model (USM) for OSNs; we introduce a kernel of a USM using standard social ontologies to support the principal social media and it can be extended to support other future social media.

Social Network Citizenship

2012 ◽  
pp. 339-357 ◽  
Author(s):  
Mina Seraj ◽  
Aysegul Toker
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Conceptual Model ◽  
Online Social Networks ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Marketing Practice

This chapter describes and discusses the specificities of membership commitment to online social networks. While delineating these specificities, we introduce the concept of social network citizenship (SNC) to define the characteristics of committed network members. A conceptual model involving commencement, creation, change, and commitment is developed in order to establish the antecedents of this new concept. In addition, the implications for marketing practice are discussed to reveal how companies can acquire social network citizens to retain their social media marketing strategies successful.

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

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