A Study

2021 ◽  
Vol 13 (1) ◽  
pp. 46-56
Author(s):  
Shivani Jain ◽  
Alankrita Aggarwal ◽  
Sandeep Mittal
Keyword(s):  
Social Media ◽  
Machine Learning Techniques ◽  
Epidemic Outbreak ◽  
Vector Borne Diseases ◽  
Related Information ◽  
Learning Techniques ◽  
Textual Data ◽  
Media Platform ◽  
Vector Borne ◽  
Media Data

Chikungunya, an infection which is difficult to treat, took a toll on Delhi in year 2016. In that scenario, detection and prevention of vector-borne diseases outbreak in Delhi have been a major cause of concern for government. For analyzing this epidemic outbreak, the authors have utilized the unstructured data generated through Twitter. Twitter is a social media platform that generates vast amount of epidemic-related information every day. This information is used to analyze the effect of epidemic outbreak in Delhi region. In this paper, the authors discussed an associated study of various machine learning techniques for analyzing and mining social media information. In this, the authors have also categorized and explore the steps involved in social media textual data to provide a pictorial view of the ongoing outbreak. Finally, the article discussed the challenges faced for mining social media data.

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.

scholarly journals Review article: Detection of informative tweets in crisis events

2020 ◽  
Author(s):  
Anna Kruspe ◽  
Jens Kersten ◽  
Friederike Klan
Keyword(s):  
Social Media ◽  
Review Article ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Semantic Classification ◽  
Crisis Events ◽  
Learning Techniques ◽  
Media Data ◽  
Crisis Situations

Abstract. Messages on social media can be an important source of information during crisis situations, be they short-term disasters or longer-term events like COVID-19. They can frequently provide details about developments much faster than traditional sources (e.g. official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing, crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

scholarly journals FEATURE BASED ANALYSIS ON THE DUPLICATE FAKE ACCOUNT ON TWITTER USING INTELLIGENT MECHANISM

2021 ◽  
Vol 9 (1) ◽  
pp. 1315-1320
Author(s):  
Dr. Mohammed Ali Alhariri
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Social Media Platform ◽  
Twitter Data ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Media Platform

The duplicate fake accounts are detected in this work the data from the social media platform is accessed. The platform choose to use the analysis on social media platform is selected as twitter. The twitter data is accessed using Twitter API, with using some selected features that remain the most appropriate regarding the reason of duplicate fake account. The feature based analysis is compared using machine learning techniques, Random Forest, Decision Tree, and SVM. The performance is further analyzed based on accuracy SVM performed 93.3% accuracy, where decision tree performed as 89.0% and random forest performed as 85.5%. The better performance observed using feature-based analysis is of SVM.  

scholarly journals Quantifying Media Effects, Its Content, and Role in Promoting Community Awareness of Chikungunya Epidemic in Bangladesh

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 84-94
Author(s):  
Mst. Marium Begum ◽  
Osman Ulvi ◽  
Ajlina Karamehic-Muratovic ◽  
Mallory R. Walsh ◽  
Hasan Tarek ◽  
...  
Keyword(s):  
Social Media ◽  
Web Based ◽  
Study Objective ◽  
The Public ◽  
Mainstream Media ◽  
Related Information ◽  
Vector Borne ◽  
Media Outlet ◽  
And Control

Background: Chikungunya is a vector-borne disease, mostly present in tropical and subtropical regions. The virus is spread by Ae. aegypti and Ae. albopictus mosquitos and symptoms include high fever to severe joint pain. Dhaka, Bangladesh, suffered an outbreak of chikungunya in 2017 lasting from April to September. With the goal of reducing cases, social media was at the forefront during this outbreak and educated the public about symptoms, prevention, and control of the virus. Popular web-based sources such as the top dailies in Bangladesh, local news outlets, and Facebook spread awareness of the outbreak. Objective: This study sought to investigate the role of social and mainstream media during the chikungunya epidemic. The study objective was to determine if social media can improve awareness of and practice associated with reducing cases of chikungunya. Methods: We collected chikungunya-related information circulated from the top nine television channels in Dhaka, Bangladesh, airing from 1st April–20th August 2017. All the news published in the top six dailies in Bangladesh were also compiled. The 50 most viewed chikungunya-related Bengali videos were manually coded and analyzed. Other social media outlets, such as Facebook, were also analyzed to determine the number of chikungunya-related posts and responses to these posts. Results: Our study showed that media outlets were associated with reducing cases of chikungunya, indicating that media has the potential to impact future outbreaks of these alpha viruses. Each media outlet (e.g., web, television) had an impact on the human response to an individual’s healthcare during this outbreak. Conclusions: To prevent future outbreaks of chikungunya, media outlets and social media can be used to educate the public regarding prevention strategies such as encouraging safe travel, removing stagnant water sources, and assisting with tracking cases globally to determine where future outbreaks may occur.

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

scholarly journals A Multi-platform Approach to Monitoring Negative Dominance for COVID-19 Vaccine-Related Information Online

2021 ◽  
pp. 1-24
Author(s):  
Paola Pascual-Ferrá ◽  
Neil Alperstein ◽  
Daniel J. Barnett
Keyword(s):  
Social Media ◽  
Adverse Events ◽  
News Media ◽  
Search Behavior ◽  
Social Media Data ◽  
Future Studies ◽  
Related Information ◽  
Youtube Videos ◽  
Negative Dominance ◽  
Media Data

Abstract Objective The aim of this study was to test the appearance of negative dominance in COVID-19 vaccine-related information and activity online. We hypothesized that if negative dominance appeared, it would be a reflection of peaks in adverse events related to the vaccine, that negative content would attract more engagement on social media than other vaccine-related posts, and posts referencing adverse events related to COVID-19 vaccination would have a higher average toxicity score. Methods We collected data using Google Trends for search behavior, CrowdTangle for social media data, and Media Cloud for media stories, and compared them against the dates of key adverse events related to COVID-19. We used Communalytic to analyze the toxicity of social media posts by platform and topic. Results While our first hypothesis was partially supported, with peaks in search behavior for image and YouTube videos driven by adverse events, we did not find negative dominance in other types of searches or patterns of attention by news media or on social media. Conclusion We did not find evidence in our data to prove the negative dominance of adverse events related to COVID-19 vaccination on social media. Future studies should corroborate these findings and, if consistent, focus on explaining why this may be the case.

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.

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