Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters.Emergency response authorities,law enforcement agencies,and the public can use this information to gain situational awareness and improve disaster response.In case of emergencies,rapid responses are needed to address victims’requests for help.The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past.However,most of the present deployments of platforms in crisis management are not automated,and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations.The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential.This research project aims to develop a generalized Information Technology(IT)solution for emergency response and disaster management by integrating social media data as its core component.In this paper,we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts.More specifically,we applied state-of-the-art Natural Language Processing(NLP),Machine Learning(ML),and Deep Learning(DL)techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis.As a proof of concept,a case study on the COVID-19 pandemic on the data collected from Twitter is presented,providing evidence that the scientific and operational goals have been achieved.

Utilizing Social Media Data Analytics to Enhance Banking Services

The two most important challenges facing banks today are attracting new customers and retaining their existing ones. Research shows that 30 percent of banks cited customer loyalty as their biggest challenges. Thus, given that customer loyalty is completely connected to customer delight. The challenging question is: How do banks achieve customer delight by making every interaction a pleasant experience? In our viewpoint “The key is to stop treating customers as segments and personalize all customer interactions and services which can be achieved by using the latest technological advancements in Big Data Analytics, Artificial Intelligence (AI) and Machine Learning”. With the rapidly increasing usage of social media like Facebook, Twitter, LinkedIn, and Instagram, business organizations are now moving towards adapting this technology to drive business advantages. This research will explore the power of social media and how it can be used by banks to provide an edge over their competitors by providing improved products and services to their customers thereby making their experience easy and responsive. It also proposes a framework for social media analytics and its important components to address all the technical and business aspects of the retail and online banking, however, what customer expects from this medium and what banks offer to them needs to be widely studied and understood.

Influence Analysis for Celebrities via Public Cloud and Social Platform

Recently, the online social networks have emerged as one of the important platforms for social users. Among millions of users, famous person from entertainment circle arouse our interest. They promote social relationship and establish their reputation via these platforms. To analyze the social influence of entertainment stars we propose and implement a public cloud based framework to crawl celebrities' social messages from Sina Weibo, store the gathered messages and conduct various analysis to assess the socia influence. It consist of three key components: task generation, resource management and task scheduling, and influence analysis. The task generation is responsible of acquiring celebrities' socia accounts and issue crawling tasks. We propose a cross-media method to extract social accounts from webpages. The resource management and task scheduling will dynamic adjust the rented resource to minimize the total computing cost while keeping Qo S. We propose a dynamic instance provisioning strategy based on the large deviation principle. The influence analysis will undertake various types of analysis, such as fan count, posting frequency, textual analysis, and so on. More than 10,000 celebrities' microblogs have been gathered so far, and some related gainers, such as celebrities and ad agencies can gain the illumination brought by our analysis.

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 pandemic.Social media has been shown to influence the low acceptance of vaccines.This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement.This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines.This paper introduces a sigmoidal particle swarm optimization(SPSO)algorithm.First,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing sentiment.The proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature selection.The Covid-19 vaccination tweets dataset was created and used for training,validating,and testing.The proposed approach outperformed considered algorithms in terms of accuracy.Additionally,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection algorithm.It shows that augmentation improves the overall accuracy of tweet analysis.After the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

A Systematic Review Towards Big Data Analytics in Social Media

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies.This new era allows the consumer to directly connect with other individuals,business corporations,and the government.People are open to sharing opinions,views,and ideas on any topic in different formats out loud.This creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data analytics.This study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data analytics.We explain why social media data are significant elements of the improved data-driven decision-making process.We propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs.We discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text Analytics"is the most used analytics in social data analysis to date.We create a taxonomy on social media analytics to meet the need and provide a clear understanding.Tools,techniques,and supporting data type are also discussed in this research work.As a result,researchers will have an easier time deciding which social data analytics would best suit their needs.

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.