Big Data and Political Social Networks

2016 ◽  
Vol 35 (1) ◽  
pp. 126-141 ◽  
Author(s):  
Axel Maireder ◽  
Brian E. Weeks ◽  
Homero Gil de Zúñiga ◽  
Stephan Schlögl
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Political Communication ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Political Issues ◽  
Transatlantic Trade ◽  
Twitter Data ◽  
Shed Light

Social media have changed the way citizens, journalists, institutions, and activists communicate about social and political issues. However, questions remain about how information is diffused through these networks and the degree to which each of these actors is influential in communicating information. In this study, we introduce two novel social network measures of connection and information diffusion that help shed light on patterns of political communication online. The Audience Diversity Score assesses the diversity of a particular actor’s followers and identifies which actors reach different publics with their messages. The Communication Connector Bridging Score highlights the most influential actors in the network who are potentially able to connect different spheres of communication through their information diffusion. We apply and discuss these measures using Twitter data from the discussion regarding the Transatlantic Trade Investment Partnership in Europe. Our results provide unique insights into the role various actors play in diffusing political information in online social networks.

scholarly journals Emotion shapes the diffusion of moralized content in social networks

2017 ◽  
Vol 114 (28) ◽  
pp. 7313-7318 ◽  
Author(s):  
William J. Brady ◽  
Julian A. Wills ◽  
John T. Jost ◽  
Joshua A. Tucker ◽  
Jay J. Van Bavel
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Political Debate ◽  
Political Issues ◽  
Emotional Words ◽  
Social Media Networks ◽  
Political Ideas ◽  
The Social ◽  
Network Methods

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

Sentiment Based Information Diffusion in Online Social Networks

2018 ◽  
Vol 8 (1) ◽  
pp. 60-74
Author(s):  
Mohammad Ahsan ◽  
Madhu Kumari ◽  
Tajinder Singh ◽  
Triveni Lal Pal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Random Forests ◽  
Presidential Election ◽  
Online Social Networks ◽  
Regression Models ◽  
Information Diffusion ◽  
Comprehensive Understanding ◽  
Contextual Features ◽  
The Relationship

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

scholarly journals Turn Threats into “Treatments”? Social Bots’ Possible Applications in Health Communication during Pandemics

2021 ◽  
Vol 2 (2) ◽  
pp. 281-288
Author(s):  
Zhenling Sun
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Health Communication ◽  
Online Social Networks ◽  
Negative Association ◽  
Study Analysis ◽  
Positive Role ◽  
Political Issues ◽  
Social Media Platforms ◽  
Cyber Space

COVID-19 pandemic is a global Crisis, social media platforms have been a significant site of getting information and arouse discussions. However, social bots have risen on the online social networks, social bots are applications that existing in cyber space merely and they can mimic human users to interact with you following their own logic, there are the features of “Intangible”、“personate” and “automatic”. Evidence suggests that social bots did harm to the Health Communication during COVID-19 pandemic, researchers found that social bots contributed to diffuse political issues stir negative emotions, spread rumor. Social bots often have a negative association, but there are many bots which perform benign tasks. This study analysis the reasons bots performed badly in COVID-19 pandemic first, then discuss about how to turn the “threats” to “treatments”, proving that social bots can act as a positive role in different periods of Health Emergencies.

Social Networking as Power Balancing in Tanzanian Politics

Utafiti ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 88-108
Author(s):  
Antoni Keya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
Political Communication ◽  
Online Social Networks ◽  
Good Governance ◽  
National Level ◽  
Positioning Theory ◽  
Political Opposition ◽  
Ordinary Citizen

Online social networks have made communication more accessible in many spheres; they have been used as an alternative political means of regaining or equalising power between a political opposition and a ruling party, when the former is functioning in the absence of a traditional political platform. Recently opposition politicians in Tanzania have utilised online social networks towards this end. Positioning theory draws out this political dynamic in operation. Historically, political communication using electronically transmitted networks in Tanzania was necessitated after the fifth-phase presidency (beginning in 2015) placed a ban on political activities reaching outside their official jurisdictions and naming specific candidates. This ban seemed to weaken opposition politicians because they were regarded as preferring to work in collectives. The analysis here focuses on a press conference involving a United Republic of Tanzania Member of Parliament from the opposition party, addressing the national and international communities in regard to secrecy that surrounded a late arrival of two new jets into Tanzania from Canada, late in 2016. The data suggests that online social networking has enabled opposition politicians to identify themselves as fellow sufferers and representatives of the ordinary citizen, demanding good governance and speaking against misappropriation and laxity in distribution and use of national resources. The opposition has gone further in utilising social media to present the nation’s presidential agenda as pitted against the ordinary citizen. Social media allows the opposition to represent the current government as an elite group responsible for the problems Tanzanians are facing, and therefore as untrustworthy. This limited case study reveals how electronic media re-introduces a potential for effective political opposition to the status quo at a national level.

Sentiment Based Information Diffusion in Online Social Networks

2020 ◽  
pp. 807-822
Author(s):  
Mohammad Ahsan ◽  
Madhu Kumari ◽  
Tajinder Singh ◽  
Triveni Lal Pal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Random Forests ◽  
Presidential Election ◽  
Online Social Networks ◽  
Regression Models ◽  
Information Diffusion ◽  
Comprehensive Understanding ◽  
Support Vectors ◽  
The Relationship

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

scholarly journals SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

2018 ◽  
Vol 10 (8) ◽  
pp. 2731 ◽  
Author(s):  
Berny Carrera ◽  
Jae-Yoon Jung
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Diffusion Process ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Probabilistic Approach ◽  
Digital Era ◽  
Online Social Media ◽  
User Comments

In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

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.

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