scholarly journals Impact of COVID-19 on Multiple Sclerosis Topic Discussion on Twitter

2021 ◽  
Author(s):  
Guido Giunti ◽  
Maëlick Claes ◽  
Enrique Dorronzoro Zubiete ◽  
Octavio Rivera-Romero ◽  
Elia Gabarron
Keyword(s):  
Multiple Sclerosis ◽  
Social Media ◽  
Comparative Analysis ◽  
Social Interaction ◽  
Healthcare Professional ◽  
Inverse Document Frequency ◽  
Original Dataset ◽  
Document Frequency ◽  
Neurologic Disorders ◽  
Emotional Aspects

Introduction: Multiple sclerosis (MS) is one of the world’s most common neurologic disorders. Social media have been proposed as a way to maintain and even increase social interaction for people with MS. The objective of this work is to identify and compare the topics on Twitter during the first wave of COVID-19 pandemic. Methods: Data was collected using the Twitter API between 9/2/2019 and 13/5/2020. SentiStrength was used to analyze data with the day that the pandemic was declared used as a turning point. Frequency-inverse document frequency (tf-idf) was used for each unigram and calculated the gains in tf-idf value. A comparative analysis of the relevance of words and categories among the datasets was performed. Results: The original dataset contained over 610k tweets, our final dataset had 147,963 tweets. After the 10th of march some categories gained relevance in positive tweets (“Healthcare professional”, “Chronic conditions”, “Condition burden”), while in negative tweets “Emotional aspects” became more relevant and “COVID-19” emerged as a new topic. Conclusions: Our work provides insight on how COVID-19 has changed the online discourse of people with MS.

scholarly journals Sentiment Analysis with Machine Learning Methods on Social Media

2020 ◽  
Vol 9 (3) ◽  
pp. 5-15
Author(s):  
Muhammet Sinan Basarslan ◽  
Fatih Kayaalp
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
The Social ◽  
Vector Machines ◽  
Artificial Neural ◽  
Use Of The Internet ◽  
Python Programming

Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.

scholarly journals Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Wahyu Adi Prabowo ◽  
Fitriani Azizah
Keyword(s):  
Social Media ◽  
System Model ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Digital Development ◽  
Social Media Platforms ◽  
Python Programming ◽  
System Model Design

Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.

AI-Based Cyberbullying Prevention in 5G Networks

2020 ◽  
Vol 11 (4) ◽  
pp. 1-20
Author(s):  
Sara Ramezanian ◽  
Tommi Meskanen ◽  
Valtteri Niemi
Keyword(s):  
Social Media ◽  
Psychological Effects ◽  
5G Networks ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Online Social Media ◽  
Document Frequency ◽  
Policy Control ◽  
Cyberbullying Detection ◽  
Victims Of Bullying

Children and teenagers that have been victims of bullying can possibly suffer its psychological effects for a lifetime. With the increase of online social media, cyberbullying incidents have been increased as well. In this paper, the authors discuss how they can detect cyberbullying with AI techniques, using term frequency-inverse document frequency. The authors label messages as benign or bully. The authors want their method of cyberbullying detection to be privacy-preserving, such that the subscribers' benign messages should not be revealed to the operator. Moreover, the operator labels subscribers as normal, bully, and victim. The operator utilizes policy control in 5G networks to protect victims of cyberbullying from harmful traffic.

scholarly journals Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 32
Author(s):  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Nur Azzah Abu Bakar ◽  
Mohamed Ahmed Tayie ◽  
Ghaith Abdulsattar A.Jabbar Alkubaisi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Semantic Analysis ◽  
Similarity Measures ◽  
Jaccard Similarity ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Sentence Level ◽  
Textual Data ◽  
Document Level

Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.  One of the reason is two sentences can convey the same meaning with totally dissimilar words.  This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences.  Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures. 

Exploiting user network topology and comment semantic for accurate rumour stance recognition on social media

2020 ◽  
pp. 016555152097744
Author(s):  
Yongcong Luo ◽  
Jing Ma ◽  
Chai Kiat Yeo
Keyword(s):  
Social Media ◽  
Network Topology ◽  
Semantic Representation ◽  
Fake News ◽  
Inverse Document Frequency ◽  
Online Social Media ◽  
Document Frequency ◽  
Rapid Dissemination ◽  
Sentiment Score ◽  
Vector Matrix

Online social media (OSM) has become a hotbed for the rapid dissemination of disinformation or fake news. In order to recognise fake news and guide users of OSM, we focus on the stance recognition of comments, posted on OSM on the fake news-related users. In this article, we propose a framework for recognition of rumour stances (we set four categories –‘agree’, ‘disagree’, ‘neutral’ and ‘query’), combining network topology and comment semantic enhancement (CSE). We first construct a vector matrix of comments via a novel optimised term frequency–inverse document frequency (OTI). To better recognise stances, we employ another vector matrix with novel or special attributes which comprises the network topology of the OSM users derived from the random walk with restart (RWR) method. In addition, we set a weight parameter for each word in the comments to enhance comment semantic representation, where these parameters are tuned based on sentiment score, topology features and question format words. These vector matrices are optimised and combined into an integrated matrix whose transpose matrix is fed into a neural network (NN) for final rumour stance recognition. Experimental evaluations show that our approach achieves a high precision of 93.96% and F1-score of 92.02% which are superior to baselines and other existing methods.

Identification of rumour stances by considering network topology and social media comments

2020 ◽  
pp. 016555152094435
Author(s):  
Yongcong Luo ◽  
Jing Ma ◽  
Chai Kiat Yeo
Keyword(s):  
Social Media ◽  
Network Topology ◽  
Inverse Document Frequency ◽  
Online Social Media ◽  
Document Frequency ◽  
The Matrix ◽  
Special Attribute ◽  
Rapid Dissemination ◽  
Two Parameters ◽  
Vector Matrix

Online social media (OSM) has become a hotbed for the rapid dissemination of disinformation or faked news. In order to track and limit the spread of faked news, we study stance identification of comments posted on OSM, where the stance can denote the comment’s semantics. In this article, we propose a framework for identification of rumour stances, combining network topology and OSM comments. We construct a vector matrix of comments and words via OTI (optimisation term frequency–inverse document frequency). To better identify the stances, we introduce another vector matrix with novel or special attribute, that is, network topology among the users. Variant autoencoder (VAE) is then applied for dimensionality reduction and optimisation of these vector matrices which are then combined into an integrated matrix [Formula: see text], tempered by two parameters [Formula: see text] and [Formula: see text]. Finally, the matrix is fed into a neural network for final rumour stance identification. Experimental evaluations show that our proposed approach outperforms some state-of-the-art methods and achieves a high precision of 90.26% and F1-score of 88.58%.

Socializing the web

2021 ◽  
Vol 27 (3) ◽  
pp. 32-36
Author(s):  
Judith Donath
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Social Applications ◽  
The Web

Though today we think of the web and social media as nearly synonymous, the technology of the early web made social interaction difficult. The author discusses her work creating some of the web's earliest social applications and asks why our interfaces for seeing and communicating with each other online are still so primitive.

scholarly journals It takes two to tango: the interplay between decision logics, communication strategies and social media engagement in start-ups

2021 ◽  
Author(s):  
Christian Rudeloff ◽  
Stefanie Pakura ◽  
Fabian Eggers ◽  
Thomas Niemand
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Exploratory Study ◽  
Communication Strategies ◽  
Qualitative Comparative Analysis ◽  
Communication Models ◽  
Social Media Engagement ◽  
Start Ups

AbstractThis manuscript analyzes start-ups’ usage of different communication strategies (information, response, involvement), their underlying decision logics (effectuation, causation, strategy absence) and respective social media success. A multitude of studies have been published on the decision logics of entrepreneurs as well as on different communication strategies. Decision logics and according strategies and actions are closely connected. Still, research on the interplay between the two areas is largely missing. This applies in particular to the effect of different decision logics and communication models on social media success. Through a combination of case studies with fuzzy-set Qualitative Comparative Analysis this exploratory study demonstrates that different combinations of causal and absence of strategy decision logics can be equally successful when it comes to social media engagement, whereas effectuation is detrimental for success. Furthermore, we find that two-way-communication is essential to create engagement, while information strategy alone cannot lead to social media success. This study provides new insights into the role of decision logics and connects effectuation theory with the communication literature, a field that has been dominated by causal approaches.

Sign in / Sign up

Export Citation Format

Share Document