scholarly journals Standing out in a networked communication context: Toward a network contingency model of public attention

2020 ◽  
pp. 146144482093944
Author(s):  
Aimei Yang ◽  
Adam J Saffer
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Public Attention ◽  
Combine Data ◽  
Contingency Model ◽  
Learning Techniques ◽  
Community Strategy ◽  
Communication Context

Social media can offer strategic communicators cost-effective opportunities to reach millions of individuals. However, in practice it can be difficult to be heard in these crowded digital spaces. This study takes a strategic network perspective and draws from recent research in network science to propose the network contingency model of public attention. This model argues that in the networked social-mediated environment, an organization’s ability to attract public attention on social media is contingent on its ability to fit its network position with the network structure of the communication context. To test the model, we combine data mining, social network analysis, and machine-learning techniques to analyze a large-scale Twitter discussion network. The results of our analysis of Twitter discussion around the refugee crisis in 2016 suggest that in high core-periphery network contexts, “star” positions were most influential whereas in low core-periphery network contexts, a “community” strategy is crucial to attracting public attention.

scholarly journals Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media Conversation

2021 ◽  
Vol 179 ◽  
pp. 821-828
Author(s):  
Andry Chowanda ◽  
Rhio Sutoyo ◽  
Meiliana ◽  
Sansiri Tanachutiwat
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Learning Techniques

A shift from science to politics : a content analysis of COVID-19 Videos on YouTube (Preprint)

2021 ◽  
Author(s):  
Chyun-Fung Shi ◽  
Matthew C So ◽  
Sophie Stelmach ◽  
Arielle Earn ◽  
David J D Earn ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Control Measures ◽  
Mitigation Measures ◽  
Health Campaigns ◽  
Public Attention ◽  
Conflicting Information ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. OBJECTIVE This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period. METHODS We analyzed top-viewed YouTube COVID-19 related videos in English from from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March. RESULTS We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period. CONCLUSIONS Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Blockchain-based Framework for Reducing Fake or Vicious News Spread on Social Media/Messaging Platforms

2022 ◽  
Vol 21 (1) ◽  
pp. 1-33
Author(s):  
Sakshi Dhall ◽  
Ashutosh Dhar Dwivedi ◽  
Saibal K. Pal ◽  
Gautam Srivastava
Keyword(s):  
Social Media ◽  
Consensus Algorithm ◽  
Machine Learning Techniques ◽  
Stock Exchanges ◽  
Fake News ◽  
Law And Order ◽  
Learning Techniques ◽  
Original Message ◽  
Media Messaging ◽  
Malicious Intent

With social media becoming the most frequently used mode of modern-day communications, the propagation of fake or vicious news through such modes of communication has emerged as a serious problem. The scope of the problem of fake or vicious news may range from rumour-mongering, with intent to defame someone, to manufacturing false opinions/trends impacting elections and stock exchanges to much more alarming and mala fide repercussions of inciting violence by bad actors, especially in sensitive law-and-order situations. Therefore, curbing fake or vicious news and identifying the source of such news to ensure strict accountability is the need of the hour. Researchers have been working in the area of using text analysis, labelling, artificial intelligence, and machine learning techniques for detecting fake news, but identifying the source or originator of such news for accountability is still a big challenge for which no concrete approach exists as of today. Also, there is another common problematic trend on social media whereby targeted vicious content goes viral to mobilize or instigate people with malicious intent to destabilize normalcy in society. In the proposed solution, we treat both problems of fake news and vicious news together. We propose a blockchain and keyed watermarking-based framework for social media/messaging platforms that will allow the integrity of the posted content as well as ensure accountability on the owner/user of the post. Intrinsic properties of blockchain-like transparency and immutability are advantageous for curbing fake or vicious news. After identification of fake or vicious news, its spread will be immediately curbed through backtracking as well as forward tracking. Also, observing transactions on the blockchain, the density and rate of forwarding of a particular original message going beyond a threshold can easily be checked, which could be identified as a possible malicious attempt to spread objectionable content. If the content is deemed dangerous or inappropriate, its spread will be curbed immediately. The use of the Raft consensus algorithm and bloXroute servers is proposed to enhance throughput and network scalability, respectively. Thus, the framework offers a proactive as well as reactive, practically feasible, and effective solution for curtailment of fake or vicious news on social media/messaging platforms. The proposed work is a framework for solving fake or vicious news spread problems on social media; the complete design specifications are beyond scope of the current work and will be addressed in the future.

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

scholarly journals Detection of Mentally Distressed Social Media Profiles Using Machine Learning Techniques

2020 ◽  
Vol 981 ◽  
pp. 022056
Author(s):  
T. Sravanthi ◽  
V. Hema ◽  
S Tharun Reddy ◽  
K Mahender ◽  
S Venkateshwarlu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Learning Techniques
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