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.

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 Understanding the Users Personal Attributes Similarity Across Online Social Networks

2019 ◽  
Vol 8 (11) ◽  
pp. 3902-3908
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Criminal Behavior ◽  
Personal Information ◽  
Personal Attributes ◽  
The Social ◽  
Media Services ◽  
The Relationship ◽  
Modern Era

In this modern era of technology, everyone accessing the Internet is obsessed with social media. A User accesses different social media services to fulfill his diverse needs. For instance, Instagram is mainly used for sharing personal visual content while Twitter is known for finding latest news and trends, similarly Facebook for personal posts. Such services lead to the distribution of personal information of an Internet user on these platforms. In this paper, we build a framework to discover the relationship among the attributes of a user across the social media.We use different fuzzy string matching algorithms to find the similarities between the attributes. We extract the ‘name’ and ‘username’ from a publicly shared dataset and apply two character based and token based algorithms on these features. The results are indicative of the fact that only a limited number of users share the same name and username across the sites. On further analysis, it is found that although name and username of most of the users do not exactly match, they tend to be similar with the infinitesimal difference like; underscore, period, one digit numbers, etc. This study provides an analysis of the typical variations in names and usernames, which can further be studied for the extension to other social networks This profile will help in behavior analysis of a user, which will further help us to improve recommendations and analyze for criminal behavior and similar applications.

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 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.

scholarly journals A Data-Driven Framework for Coding the Intent and Extent of Political Tweeting, Disinformation, and Extremism

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 148
Author(s):  
Mahdi Hashemi
Keyword(s):  
Social Networks ◽  
Presidential Election ◽  
Online Social Networks ◽  
Data Driven ◽  
The Political ◽  
Political Affiliation ◽  
News Sources ◽  
The U.S

Disinformation campaigns on online social networks (OSNs) in recent years have underscored democracy’s vulnerability to such operations and the importance of identifying such operations and dissecting their methods, intents, and source. This paper is another milestone in a line of research on political disinformation, propaganda, and extremism on OSNs. A total of 40,000 original Tweets (not re-Tweets or Replies) related to the U.S. 2020 presidential election are collected. The intent, focus, and political affiliation of these political Tweets are determined through multiple discussions and revisions. There are three political affiliations: rightist, leftist, and neutral. A total of 171 different classes of intent or focus are defined for Tweets. A total of 25% of Tweets were left out while defining these classes of intent. The purpose is to assure that the defined classes would be able to cover the intent and focus of unseen Tweets (Tweets that were not used to determine and define these classes) and no new classes would be required. This paper provides these classes, their definition and size, and example Tweets from them. If any information is included in a Tweet, its factuality is verified through valid news sources and articles. If any opinion is included in a Tweet, it is determined that whether or not it is extreme, through multiple discussions and revisions. This paper provides analytics with regard to the political affiliation and intent of Tweets. The results show that disinformation and extreme opinions are more common among rightists Tweets than leftist Tweets. Additionally, Coronavirus pandemic is the topic of almost half of the Tweets, where 25.43% of Tweets express their unhappiness with how Republicans have handled this pandemic.

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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