Topics In Twitter Research Articles

The public’s appropriation of multimodal discourses of fake news on social media

ABSTRACT This study empirically examines tweets and Instagram posts that reference the hashtag #fakenews in connection to Canadian issues to understand the nature of the public’s political and multimodal discourses. Taken from larger datasets consisting of over 255,000 Instagram posts and over 14 million tweets, we used a mixed method, partly analyzing more than 4100 most retweeted messages and Instagram posts and manually categorizing them into seven topic types along with their political tone. Theoretically, we argue that the term fake news has lost its core meaning as it is appropriated by the social media public to communicate a variety of messages especially in relation to politics. The findings show that although there are differences between the two social media platforms, the majority of Instagram and Twitter topics that reference fake news are political in nature and anti-liberal in tone. Methodologically, the inclusion of multimodal analysis helps identify the sentiment and emotional aspects which are critical aspects for the spread of fake news and polarization on social media. Despite the different political contexts, our findings on Instagram and Twitter align with other studies that examined political polarization and the prevalence of conservative voices in the United States.

Realizing Emergent Topics in Twitter by Detection of Abnormality Links

In the present paper, we demonstrated the probability model which may capture the usual mentioning behavior of an end user consisting of both the number of mentions for every post and the frequency of users occurring in the mentions. Subsequently, to compute the anomaly of future user behavior this model is needed. While using the proposed probability model, we can quantitatively compute the originality or probable effect of a post resembled in the mentioned behavior of the end user. We aggregate all the anomaly scores obtained in this way above countless end users. The efficiency of the proposed method is demonstrated on four data sets we have obtained through Twitter. We demonstrated that the mention-anomaly-based method combined with the TFIDF method can detect the emergence of a new topic at least as quickly as text-anomaly-based counterparts.

Discursive dynamics and local contexts on Twitter: The refugee crisis in Europe

In today’s hybrid media environment, traditional news organizations extend their presence on Online Social Networks (OSNs) and compete with political and civil society organizations, public figures, and other influential digital storytelling individuals. This article examines conversations on Twitter, one of the most widely used OSNs, about Europe’s refugee crisis in 2014 and 2015. We use, in particular, topic modeling techniques to deduce the existence of a complex network of Twitter topics formed in response to coverage of and opinion formation surrounding the European refugee crisis. We collected more than 11 million tweets in six different languages. One of our most significant findings is that while most conversations happen in English, the refugee crisis has had different rhythms in other languages. Our assumption is that this could be evidence that the power of mainstream local media on Twitter to set the agenda is considerable, at least regarding refugee-related conversations in Europe.

Bitcoin Price Prediction and NFT Generator Based on Sentiment Analysis

Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.

Deep learning-based user experience evaluation in distance learning.

The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

Social Network Analysis of The Development of The Halal Industry In Indonesia


 
 
 
 This study aims to look at the public sentiment represented by Twitter users regarding the halal industry. Data was taken using Drone Emprit Academic, a big data method that captures and analyzes conversations on social media, especially on Twitter, developed by Media Kernels Indonesia, which is also installed on the Information System Agency of the Islamic University of Indonesia. The research method uses a social network analysis approach to analyze data on social media conversations. The data was obtained after observing for 30 days from trending Twitter topics. The data is processed by the Social Network Analysis (SNA) system, which can be interpreted as a description of the interactions and relationships that always occur between one individual and another in an organization or work environment and the company. We found that the halal industry in Indonesia is growing more rapidly with the existence of social networks. A large number of conversations among Twitter users in Indonesia shows this.
 
 
 

Exploring public values through Twitter data associated with urban parks pre- and post- COVID-19

Since school and business closures due to the evolving COVID-19 outbreak, urban parks have been a popular destination, offering spaces for daily fitness activities and an escape from the home environment. There is a need for evidence for parks and recreation departments and agencies to base decisions when adapting policies in response to the rapid change in demand and preferences during the pandemic. The application of social media data analytic techniques permits a qualitative and quantitative big-data approach to gain unobtrusive and prompt insights on how parks are valued. This study investigates how public values associated with NYC parks has shifted between pre- COVID (i.e., from March 2019 to February 2020) and post- COVID (i.e., from March 2020 to February 2021) through a social media microblogging platform –Twitter. A topic modeling technique for short text identified common traits of the changes in Twitter topics regarding impressions and values associated with the parks over two years. While the NYC lockdown resulted in much fewer social activities in parks, some parks continued to be valued for physical activity and nature contact during the pandemic. Concerns about people not keeping physical distance arose in parks where frequent human interactions and crowding seemed to cause a higher probability of the coronavirus transmission. This study demonstrates social media data could be used to capture park values and be specific per park. Results could inform park management during disruptions when use is altered and the needs of the public may be changing.

Correction to: Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Correction to: Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Query-oriented topical influential users detection for top-k trending topics in twitter

Query-oriented topical influential users detection for top-k trending topics in twitter

토픽 모델링으로 살펴본 한국사회의 기본소득 담론 전개*

This study explores the discourses on basic income in academic, media, and civil society domains to expand the current landscape of the discussions on basic income and elicit effective political intervention. We analyze the main issues and topics in academic papers, news articles, and Twitter using topic modeling techniques. The result shows that the discussion on basic income in Korean society is developing in various ways according to the academic, media, and civil society fields. In the academic field, attention was paid to the legal/institutional, ideological, and philosophical foundations of basic income, while in the media and civil society fields, the main discussion was on the legislation or implementation of basic income-related policies such as youth dividend. More importantly, the discourses on basic income have changed significantly over time. In line with the social changes accompanied by the 4th industrial revolution, basic income has been recognized as a measure that counters the transitions in the labor market structure. In addition, the national crisis caused by COVID-19 in 2020 has accelerated the immediate application of basic income such as disaster subsidies. This research suggests that the series of social changes have led basic income to be acknowledged as a universal social policy beyond ideological inclination.

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

While the salience of social media platforms on modern interactive communication between diverse social actors has been demonstrated, less academic attention has been paid to comparisons between framed topics and user interactions across social media platforms, such as Twitter and Weibo. This article suggests text mining and natural language processing tools for cross-platform comparative social media studies, based on Latent Dirichlet Allocation (LDA) and social network analysis. This study illustrates how the suggested topic models and data processing algorithms can be applied to a real-life example (U.S.-China trade war discourse on social media), and experimented the methods on social media text mining data, revealing differences between user interactions on Twitter, predominantly “Western,” and Weibo, largely representing Chinese-speaking users. We discuss the strengths and weaknesses of the suggested machine learning algorithms for comparative social media studies.

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

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.

Real Time Data Pipeline for Twitter Trends Analysis

In social media, Information is present in enormous amount. Extracting data from processed information from social media gives us diverse usages in various fields. In the field of Business Analytics, HealthCare, Technologies and Trending Topics in Social Media posted by the user. Extracting information from social media is providing number of benefits such as knowledge about the latest Technology, Medical field, Business Decisions, etc. Twitter is solitary of the social media which allows the user post tweets of limited number of characters and share the tweet to their followers. Twitter allows application developer to access the tweets for their motive. In the implemented methodology, Tweets are collected, and sentiment analysis is performed on them. Based on the results of sentimental analysis of Trending Topics in Twitter, suggestions can be provided to the user. In this way, the implemented system can help in improving the growth of business, healthcare, technologies and alsoNegative or Positive mentions of a product or service can be determined.

Discovering topics in Twitter about the COVID-19 outbreak in Spain

In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic More particularly, we explore the period of time that includes three differentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown To do so, we first collect a large corpus of Spanish tweets and clean them Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and define generative and discriminative routes to later extract the most relevant keywords and sentences for each topic Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at different stages of the crisis © 2021 Sociedad Espanola para el Procesamiento del Lenguaje Natural

Mining Twitter to Detect Hotspots in Psychology

Abstract. For identifying psychological hotspot topics, a mere focus on bibliometric data suffers from a publication delay. To overcome this issue, we introduce Twitter mining of ongoing online communication among scientists for the detection of psychological research topics. Specifically, we collected the entire 69,963 tweets posted between August 2007 and July 2020 from 139 accounts of psychology professors, departments, and research institutes from the German-speaking countries, as well as sections of the German Psychological Society (DGPs). To examine whether Twitter topics are hotspots in terms of indicating future publication trends, 346,361 references in the PSYNDEX database were extracted. For determining the additional value of our approach in contrast to traditional conference analysis, we gathered all available conference programs of the DGPs and its sections since 2010 and compared dates of topic emergence. Results revealed 21 topics addressing societal issues (e.g., COVID-19), methodology (e.g., machine learning), scientific research (e.g., replication crisis), and different areas of psychological research. Ten topics indicated an increasing publication trend, particularly topics related to methodology or scientific transparency. Seven Twitter topics emerged earlier on Twitter than at conferences. A total of four topics could be expected neither by bibliometric forecasting nor conference contents: “methodological issues in meta-analyses”, “playfulness”, “preregistration”, and “mobile brain/body imaging”. Taken together, Twitter mining is a worthwhile endeavor for identifying psychological hotspot topics, especially regarding societal issues, novel research methods, and research transparency in psychology. In order to get the most comprehensive picture of research hotspots, Twitter mining is recommended in addition to bibliometric analyses of publication trends and monitoring of conference topics.

PERAN BUZZER MEDIA SOSIAL DALAM MEMPERKUAT EKOSISTEM PEMASARAN DIGITAL

This article explains the role of social media buzzers that discuss the Pasar Kakilangit Mangunan Bantul to residents through hashtags which have become trending on Twitter topics. With creative content such as; videos, memes and texts, social media buzzers receive unique approval from Kakilangit Market to citizens. Through the hashtag # PasarKakilangit which can be included in the Twitter Indonesia trending topic, the buzzers together support the promotion of Pasar Kakilangit products to viral and widely known by citizens. The presence of social media buzzers has built a new culture and new knowledge of the importance of trending Twitter topics in increasing the promotion of digital products and services. This article proves that media buzzer has supported positively in strengthening the digital marketing ecosystem.

A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis

Nowadays, opinion mining and sentiment analysis are the hot topics of research in social networks. Twitter is a popular social media that has general way of expressing opinions and interacting with other users in the online. Since the topics in Twitter are very diverse, it is difficult to collect data in the domain of sentiment classification. In this paper, a novel framework has been proposed which preprocesses information to enrich the tweet. Once the tweets are processed, a various features are extracted from the tweets. In this paper, we propose hybrid machine learning algorithms called LAN2FIS (logistic adaptive network based on neuro-fuzzy inference system) to utilize this huge information. The major challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes the system complex and difficult to train the learning algorithms in a feasible time and degrades the system model in terms of classification accuracy. Hence, feature selection becomes an essential task in proposing robust and efficient classification models while increasing the accuracy. This paper presents a bi-objective optimization (minimum redundancy and maximum relevancy) for feature selection, which finds the more efficient feature subsets can be obtained. We performed our experiments using public tweets. Our overall work is implemented in a parallel and distributed way using the Hadoop framework with the MongoDB database to solve the problem of the computation time of the analysis when the dataset of the tweets is very large. Finally, we evaluate the following the performance in terms of accuracy, precision, recall, F-measure and error rate. Experimental evaluations show that our H-MLA is more efficient and has higher accuracy than that of other classifiers.

Suspicion Begets Idle Fears– an Analysis of COVID-19 Related Topics in Japanese Media and Twitter

The purpose of this study is to examine how content about the global coronavirus (COVID-19) outbreak changed in the Japanese information environment through a comparison of the topics that were prevalent in traditional media and social media. The paper investigated how the topics of information regarding the novel coronavirus (COVID-19) in newspapers and Twitter differed, how the topics in relation to COVID-19 of newspapers and Twitter users changed before and after the WHO pandemic declaration, with the assumptions that topics that are relevant to the central government of Japan are more often found in newspapers, rather than Twitter and topics that are relevant to Japanese families and individuals are more often found in Twitter, rather than newspapers. A total of 10953 newspaper articles and 171,996 tweets before and after the pandemic declaration by the WHO. Instead of employing traditional content analysis methods, this paper analyzed media content through the topicmodels package for R, for classifying the various topics that were prominent before and after the pandemic declaration due to the size of the data accumulated. The analysis of this paper found that Twitter tweets focused more on topics that affect individual or household needs such as medical issues and financial well-being of individuals and families. In contrast newspapers focused more on international topics such as China, U.S. and Europe and topics that involved the government such as the suspension of primary and secondary schools, the economy and the Tokyo Olympic games. Before the pandemic declaration by the WHO, medical topics were found to be prevalent on Twitter whereas newspaper topics were focusing on the Diamond Princess cruise ship quarantine and the current situation in other countries. After the pandemic declaration, Twitter tweets were about medical topics, but were more focused on the lack of medical supplies and future concerns of hospitals being overwhelmed leading to a collapse of the medical system in Japan. Twitter tweets also centered around hopes of support by the government, such as temporary cash payments. Newspapers on the other hand centered around topics of cancelled sports events and government announcements. The findings of this paper suggest during pandemics, mass media may be insufficient in resolving the discontent towards lack of information and suspicion arises towards the government. Twitter might be used for searching information that is not found in mass media such as newspapers as doubt towards the government rises as illustrated in the findings of this analysis.

What and With Whom? Identifying Topics in Twitter Through Both Interactions and Text

The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: Tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results against TREC2014 and our self collected tweetMarch datasets demonstrate that the proposed method is able to provide more than 30 percent improvement compared to other advanced topic derivation methods.

CLASSIFICATION AND RANKING OF TRENDING TOPICS IN TWITTER USING TWEETS TEXT

Every day millions of twitter user tweet their views on various topics using short messages of 140 to 280 characters length. Here we monitor the public opinion in twitter thereby identifying trending topics. To identify the current trending topics on Twitter, tweet classification is done. Tweet classification is a process of classifying the tweets based on the topics using the keywords of the tweets as a feature. Tweets are extracted from twitter using Twitter API. In the existing system, trending topics in twitter can be identified by hash tags. This work is going to categorize the tweets into seventeen classes and identify the trending topics by using words. Dataset used in this work is the list of tweets taken for two to three days from Twitter. By classifying tweets based on sensitive words for a certain time, top k most trending words from a topic will be obtained that convey the dynamic trend