scholarly journals SENTIMENT ANALYSIS OF CURRENT TRENDING TOPICS ON TWITTER USER BASE

2021 ◽  
Vol 16 (7) ◽  
Author(s):  
Zeeshan Rasheed
Keyword(s):  
Social Media ◽  
Strong Influence ◽  
Social Situation ◽  
Business Growth ◽  
Data Set ◽  
Twitter User ◽  
Sql Database ◽  
The World ◽  
Trending Topics ◽  
Twitter Users

Twitter has now become the most common social platform to express views on any topic. A micro-blogging social media offers a way for people around the world to show their sentiments about any political, social and cultural subject of the time. In this paper, the sentimental analysis approach has been used to analyze the positive and negative sentiments of Twitter users about some top trending #tags around the globe. The data has been collected between the duration of March to April 2021. The collected data were processed by using the Python program and then transformed our data set with the help of the SQL database. We have used graphs and tables to present the data, collected under three hashtags; which were top trending topics on that particular era. The tweets were elaborated by positive, negative and neutral sentiments which were depicted in graphs. It is clear from the results and comparison that social media has a strong influence in the present era and can be highly helpful to use as a predictor of any political, social situation prevailing in any country or worldwide. It has also been helpful for business communities to analyze their products in the same manner to improve their business growth.

Twitter users exhibited coping behaviours during the COVID-19 lockdown: an analysis of tweets using mixed methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruchi Mittal ◽  
Wasim Ahmed ◽  
Amit Mittal ◽  
Ishan Aggarwal
Keyword(s):  
Data Analysis ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Twitter Data ◽  
Long Run ◽  
The World ◽  
Twitter Users ◽  
The Government ◽  
Using Data

Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

scholarly journals Critical Discourse Analysis: Hegemony of the Social Media Twitter About National Issues in Indonesia and its Implications to the Discourse Analysis Subject in Colleges

2015 ◽  
Vol 2 (2) ◽  
pp. 153-166
Author(s):  
Elvi Susanti
Keyword(s):  
Social Media ◽  
Discourse Analysis ◽  
Critical Discourse Analysis ◽  
Text Analysis ◽  
Critical Discourse ◽  
The Social ◽  
Media Services ◽  
The World ◽  
Trending Topics ◽  
Discourse Practice

Abstract This research is linked with Twitter, as one of social media services on the Internet that are extremely popular in the world, including in Indonesia. This research is important because Twitter is effective in quickly and accurately delivering messages. In fact, everyone can act as a 'reporter' and form quick opinions through this social media. This research is aimed to investigate the emergence of the roots of hegemony based on text analysis that is linked with representation, relation, identity, and transformation of national issues that become trending topics on Twitter. Moreover, the research is to discuss the social media's discourse practice that influences media workers in producing news, and to see how it implicates the research on the study of discourse analysis. By using the Fairclough theory, especially on text analysis that is linked with representation, relation, and identity, the researcher attempts to explore how the roots of hegemony emerge in the national issues that become trending topics on Twitter. The researcher also offers a new function to complete the approach of Fairclough in text analysis on social media: transformation – which is an attempt to see the change in roles of news participants and amateur readers as 'reporters' and participate in forming opinions. Abstrak Penelitian ini berhubungan dengan twitter, sebagai salah satu media sosial di internet yang sangat populer di dunia, termasuk di indonesia. Penelitian ini penting karena twitter efektif dalam menyampaikan pesan dengan cepat dan akurat. Faktanya, semua orang dapat bertindak sebagai "reporter" dan membuat opini yang cepat melalui sosial media tersebut. Penelitian ini bertujuan untuk menyelidiki kemunculan dari akar hagemoni berdasarkan analisis teks yang berhubungan dengan representasi, hubungan, identitas, dan transformasi isu-isu nasional yang menjadi topik yang sedang tren di twitter. Selain itu, penelitian ini juga untuk mendiskusikan praktik wacana media sosial  yang mempengaruhi pekerja media dalam membuat berita, dan untuk melihat bagaimana hal tersebut melibatkan penelitian dalam studi analisis wacana. Dengan menggunakan teori Fairclough, khususnya pada analisis teks yang berhubungan dengan penafsiran, hubungan, identitas, peneliti berupaya untuk menyelidiki bagaimana akar hegemoni muncul yang menjadi topik tren di twitter. Peneliti juga menawarkan sebuah fungsi baru untuk melengkapi pendekatan Fairlclough dalam analisis teks pada sosial media: transformasi - yang merupakan usaha untuk melihat perubahan peran pembuat berita dan pembaca awam sebagai 'reporter' dan berpartisipasi dalam membentuk opini. How to Cite : Susanti, E. (2015). Hegemony of The Social Media Twitter About National Issues in Indonesia and Its Implications to the Discourse Analysis Subject in Colleges. TARBIYA: Journal Of Education In Muslim Society, 2(2), 153-166. doi:10.15408/tjems.v2i2.3180. Permalink/DOI: http://dx.doi.org/10.15408/tjems.v2i2.3180

scholarly journals Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis

2021 ◽  
pp. 1-11
Author(s):  
Kristen Weidner ◽  
Joneen Lowman ◽  
Anne Fleischer ◽  
Kyle Kosik ◽  
Peyton Goodbread ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Technology Implementation ◽  
Scale Up ◽  
Media Content Analysis ◽  
Data Set ◽  
Implementation Framework ◽  
Health Crisis ◽  
Wide Scale ◽  
Twitter Users

Purpose Telepractice was extensively utilized during the COVID-19 pandemic. Little is known about issues experienced during the wide-scale rollout of a service delivery model that was novel to many. Social media research is a way to unobtrusively analyze public communication, including during a health crisis. We investigated the characteristics of tweets about telepractice through the lens of an established health technology implementation framework. Results can help guide efforts to support and sustain telehealth beyond the pandemic context. Method We retrieved a historical Twitter data set containing tweets about telepractice from the early months of the pandemic. Tweets were analyzed using a concurrent mixed-methods content analysis design informed by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results Approximately 2,200 Twitter posts were retrieved, and 820 original tweets were analyzed qualitatively. Volume of tweets about telepractice increased in the early months of the pandemic. The largest group of Twitter users tweeting about telepractice was a group of clinical professionals. Tweet content reflected many, but not all, domains of the NASSS framework. Conclusions Twitter posting about telepractice increased during the pandemic. Although many tweets represented topics expected in technology implementation, some represented phenomena were potentially unique to speech-language pathology. Certain technology implementation topics, notably sustainability, were not found in the data. Implications for future telepractice implementation and further research are discussed.

scholarly journals Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253300
Author(s):  
Md Shoaib Ahmed ◽  
Tanjim Taharat Aurpa ◽  
Md Musfique Anwar
Keyword(s):  
Social Media ◽  
Public Perceptions ◽  
Intervention Strategies ◽  
User Generated Content ◽  
Time Intervals ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Significant Public Health ◽  
Trending Topics ◽  
Twitter Users

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one’s viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public’s thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users’ sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.

scholarly journals Incidental Effects of Automated Retweeting: An Exploratory Network Perspective on Bot Activity During Sri Lanka’s Presidential Election in 2015

2017 ◽  
Vol 37 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Chamil Rathnayake ◽  
Wayne Buente
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Sri Lankan ◽  
Eigenvector Centrality ◽  
Data Set ◽  
Political Processes ◽  
User Expectations ◽  
Twitter Users ◽  
And Behavior

The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction.

scholarly journals Assessment of Antipsychotic Medications on Social Media: Machine Learning Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Miguel A. Alvarez-Mon ◽  
Carolina Donat-Vargas ◽  
Javier Santoma-Vilaclara ◽  
Laura de Anta ◽  
Javier Goena ◽  
...  
Keyword(s):  
Social Media ◽  
Negative Impact ◽  
Online Environment ◽  
First Line ◽  
Antipsychotic Medications ◽  
Twitter User ◽  
Medical Institutions ◽  
Twitter Users ◽  
User Perspectives ◽  
First Line Treatment

Background: Antipsychotic medications are the first-line treatment for schizophrenia. However, non-adherence is frequent despite its negative impact on the course of the illness. In response, we aimed to investigate social media posts about antipsychotics to better understand the online environment in this regard.Methods: We collected tweets containing mentions of antipsychotic medications posted between January 1st 2019 and October 31st 2020. The content of each tweet and the characteristics of the users were analyzed as well as the number of retweets and likes generated.Results: Twitter users, especially those identified as patients, showed an interest in antipsychotic medications, mainly focusing on the topics of sexual dysfunction and sedation. Interestingly, paliperidone, despite being among one of the newest antipsychotics, accounted for a low number of tweets and did not generate much interest. Conversely, retweet and like ratios were higher in those tweets asking for or offering help, in those posted by institutions and in those mentioning cognitive complaints. Moreover, health professionals did not have a strong presence in tweet postings, nor did medical institutions. Finally, trivialization was frequently observed.Conclusion: This analysis of tweets about antipsychotic medications provides insights into experiences and opinions related to this treatment. Twitter user perspectives therefore constitute a valuable input that may help to improve clinicians' knowledge of antipsychotic medications and their communication with patients regarding this treatment.

scholarly journals Spam Detection Framework for Twitter using ML

2020 ◽  
Vol 9 (6) ◽  
pp. 216-219
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Bayes Theorem ◽  
Spam Detection ◽  
Bayes Classifier ◽  
Common Mode ◽  
Basic Framework ◽  
Trending Topics ◽  
Twitter Users ◽  
Promotional Messages

Spam has become one of the growing issues in social media websites. Some of the users in these websites creates spam news. Coming to twitter, Users inject tweets in trending topics and replies with promotional messages providing links. A large amount of spam has been noticied in twitter. It is necessary to identify these spams tweets in a twitter stream. Now a days ,a big part of people rely on content available in social media in their decisions, so detecting and deleting these spam details is very important. A basic framework is suggested to detect malicious account holders in twitter..At present to detect these spam users or accounts there are methods which are based on content based features, Graph based features. The system which is going to be created works on machine learning based algorithms. These algorithms help to give accurate results. In this system algorithm named Naïve Bayes classifier algorithm is going to be used. This algorithm is said to be combination of many other principles relyingupon “Bayes theorem” wherein the methods share a common mode of working.

scholarly journals Trend of Social Media News: A Viewpoint of COVID-19 Tweets Using Natural Language Processing

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Developed Countries ◽  
World Health ◽  
The World ◽  
Twitter Users ◽  
The Developed Countries ◽  
Health Organization

The meteoric rise of social media news during the ongoing COVID-19 is worthy of advanced research. Freedom of speech in many parts of the world, especially the developed countries and liberty of socialization, calls for noteworthy information sharing during the panic pandemic. However, as a communication intervention during crises in the past, social media use is remarkable; the Tweets generated via Twitter during the ongoing COVID-19 is incomparable with the former records. This study examines social media news trends and compares the Tweets on COVID-19 as a corpus from Twitter. By deploying Natural Language Processing (NLP) methods on tweets, we were able to extract and quantify the similarities between some tweets over time, which means that some people say the same thing about the pandemic while other Twitter users view it differently. The tools we used are Spacy, Networkx, WordCloud, and Re. This study contributes to the social media literature by understanding the similarity and divergence of COVID-19 tweets of the public and health agencies such as the World Health Organization (WHO). The study also sheds more light on the COVID-19 sparse and densely text network and their implications for the policymakers. The study explained the limitations and proposed future studies.

Hashtag Diplomacy and Its Use in Africa

2020 ◽  
Vol 65 (2) ◽  
pp. 133-156
Author(s):  
Suzanne Graham
Keyword(s):  
Social Media ◽  
Public Diplomacy ◽  
International Policy ◽  
Policy Actors ◽  
The World ◽  
Media Platform ◽  
Internet Penetration ◽  
Twitter Users ◽  
Heads Of State ◽  
Foreign Ministries

"Hashtag diplomacy or ‘Twitter’ diplomacy, sometimes referred to as twiplomacy, is an emerging tool used by international policy actors, such as heads of state and diplomats, to conduct public diplomacy and to reach out to worldwide audiences. In a 2018 study of government Twitter users around the world, 951 Twitter accounts were identified as belonging to state leaders and foreign ministries in 187 countries. Therefore, close to 100% of United Nations members states (193 members) consider Twitter to be a valid enough tool to employ on a frequent basis. These users have a combined audience of close to 490 million followers. But what of Twitter’s value for individual diplomats, foreign ministries and heads of state and government in Africa? Can this tool be of use in the management and implementation of public diplomacy in a continent where internet penetration is 40% of the combined population and if so, what are these foreign policymakers using it for? This chapter intends to address these questions by exploring the use of hashtag diplomacy in Africa, and other regions, in reference to select examples. It will begin by situating this type of diplomacy under the umbrella of public diplomacy and it will then move on to consider feasibility and reach of this social media platform in Africa. Keywords: hashtag/Twitter diplomacy; Africa; social media "

scholarly journals Understanding users Display-Name Consistency across Social Networks

2019 ◽  
Vol 8 (5S3) ◽  
pp. 471-476
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
Experimental Results ◽  
Personal Attributes ◽  
Media Content ◽  
Social Networking Services ◽  
Twitter User ◽  
The Social ◽  
Twitter Users

Online users create their profiles on numerous social platforms to get benefits of various types of social media content. During online profile creation, the user selects a username and feeds his/her personal details like name, location, email, etc. As different social networking services acquire common personal attributes of the same user and present them in a variety of formats. To understand the availability and similarity of personal attributes across various social networking services, we propose a method that uses the different distance measuring algorithms to determine the display-name similarity across social networks. From the experimental results, it is found that at least twenty percent GooglePlus-Facebook and Facebook-Twitter users select the same display name, while forty five percent Google and Twitter user select identical name across both the social networks.

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