Twitter Tweets Research Articles

Code-switching in South Asian English CMC

Abstract This paper analyses the use of indigenous language elements including code-switching in two contrasting genres, i.e. group chats and Twitter memes along with tweets, in the English communication of South Asian (Bangladeshi, Indian, Pakistani, and Sri Lankan) internet users. The results from group chats show that one of the most common lexical indigenous elements are tags, for example, address forms like yaar, machan, and da which all can be translated to English as ‘dude’ or ‘buddy’. The analysis of Twitter memes along with tweets shows that despite the tweet text being in English, the South Asian users tend to employ memes with indigenous text more often as compared to English memes for political satire. Overall, the study finds that code-switching and indigenous resources are used to create a sense of localness in English communication, whether it is group chats or Twitter memes and tweets.

Stock prediction using Bi-LSTM and RoBERTa sentiment analysis

Stock price forecasting is a widespread research issue in the field of finance. Stock price prediction can give investors more accurate advice and yield higher returns. With the popularity of social media, more and more people choose to express their opinions, and more and more media outlets published news on social media. This paper mainly conducts stock analysis and prediction by presenting the RoBERTa-BiLSTM model with the Bahdanau attention mechanism. It consists of two existing models: the RoBERTa and bidirectional LSTM models. The experimental results show that this model is more accurate in predicting stock prices than the Multi-Layer Perception (MLP) and LSTM models. Inputs to the model include historical AAPL stock price data and sentiment data analysed by news and Twitter tweet sentiment. In this paper, the high-precision RoBERTa-BiLSTM model for stock prediction was proposed, which provides a solution for stock prediction. The practical applications and the typical problem of most studies about stock prediction called the time-shifted problem, are provided in this paper and also have research value.

Muslims and social media: A scoping review

ABSTRACT As Muslim individuals, communities, and institutions have been transformed by the digital revolution, a literature has developed that seeks to contribute knowledge about these changes. Pioneering studies in this field suggest that this literature has a focus on digital documents. This scoping literature review aims to find out whether this focus is still valid. Therefore, we examine all studies on Muslims and social media that can be retrieved from the databases Scopus, Web of Science, Index Islamicus and Atla Religion Database between 2010 and 2022, using the following keywords: Facebook; Twitter (tweets); YouTube; Instagram; TikTok; Snapchat; and Telegram (n = 359). Our findings show that interest in studying Muslims on social media has grown significantly in recent decades and that most studies have focused on non-conflictual use of social media. Most of the studies are corpus studies, i.e. big data, data scraping, or descriptive analysis of websites, Twitter accounts, or Facebook pages (82 percent). Thus, this literature review shows that the study of Islam, Muslims, and Islamic online environments is flourishing in various fields of scholarship. However, the strong focus on descriptive documentary studies should be complemented by more studies that collect new empirical data through interviews, surveys, and mixed methods. It is only by engaging with the users of these services that we can answer when, what, and why individuals do or do not do something on social media.

Fine-Grained Sentiment Classification Using Generative Pretrained Transformer

Social media platforms have seen a significant increase in the number of users and content in recent years. Owing to the increased usage of these platforms, incidents of teasing, provocation—both positive and negative—and harassment, and community attacks have increased tremendously. There is an urgent need to automatically identify such content or tweets that can hamper the well-being of an individual or society. Analyzing social media messages from Twitter and Facebook has become the focus of sentiment analysis in recent years, which formerly focused on online product evaluations. Sentiment analysis is used in a wide range of fields besides product reviews, including harassment, stock markets, elections, disasters, and software engineering. After the tweets have been preprocessed, the extracted features are categorized using classifiers like decision trees, logistic regression, multinomial nave Bayes, support vector machines, random forests, and Bernoulli nave Bayes, as well as deep learning techniques like recurrent neural network (RNN) models, long short-term memory (LSTM) models, bidirectional long short-term memory (BiLSTM) models, and convolutional neural network (CNN) model for sentiment analysis. In this paper, different techniques are compared to classify Twitter tweets into three categories: “positive,” “negative,” and “neutral.” We proposed a novel data-balancing technique for text classification. A text classification technique is proposed for analyzing textual data using the Generative Pretrained Transformer model owing to its contextual understanding and more realistic data generation capability. Comparative analysis of different Machine learning and Deep learning models are performed with and without data balancing. The experiments show that the accuracy and F1-measure of the Twitter sentiment classification classifier are improved. The proposed ensemble has outperformed and achieved an accuracy of 90%, precision of 88%, and 81% F1 score.

Leveraging Social Media to Gauge Public Opinion on Waste Electrical and Electronic Equipment Management

In recent years, the rapid advancement of technology has led to a significant increase in the global use of electrical and electronic devices. This has resulted in a surge in the generation of waste electrical and electronic equipment (WEEE), a critical component of the complex solid waste management process. Since consumers play a major role in generating WEEE, understanding their perspectives on WEEE disposal is essential for developing effective management strategies. This study employs a combined approach utilizing quantitative methods, such as the analysis of 2,905,579 Twitter tweets collected from May 2019 to April 2022, and the fuzzy analytic hierarchy process (FAHP) technique, to identify appropriate end-of-life (EOL) options for WEEE. While there have been numerous studies on factors influencing consumer participation in WEEE disposal programs, no prior research has focused on identifying suitable EOL options for WEEE. A review of the literature revealed that factors such as ease of access to recycling centers, awareness, distrust in governments, financial incentives, charitable contributions, and concerns about data disclosure influence consumers’ decisions to participate in WEEE disposal programs. Additionally, four EOL options, namely re-use, remanufacturing, recycling, and disposal, were suggested for these products. Subsequently, the FAHP technique was used to examine the relationships between these options, and a suitable EOL option was recommended for each factor. The findings of this research can serve as a valuable tool in the WEEE management process, aiding in the development of effective strategies for WEEE disposal and contributing to a more sustainable approach to handling electronic waste.

Analyzing Public Perceptions and User Sentiments on Tweets: A Machine Learning Approach

The blast of social media has driven to a tremendous sum of user-generated information, especially on stages like Twitter where individuals express their sees and feelings openly. Opinion mining develops as a capable instrument to extricate profitable bits of knowledge from this unstructured information. Twitter serves as a rich resource for comprehending user sentiments and public viewpoints across diverse domains such as politics, current events, consumer behaviors, and brand perception. With the exponential growth in tweet volume, manual analysis of this vast dataset proves impractical. This paper digs into opinion investigation strategies particularly custom fitted for Twitter. Therefore, we are going to analyze different opinions or emotions of users and for this we will use a sentiment analysis approach. Sentiment analysis is an approach to analyzing data and capturing the emotions it embodies. Twitter Sentiment Analysis is the application of sentiment analysis to data from Twitter (Tweets) to extract the sentiments sent by users. The paper moreover examines common assessment measurements utilized to survey the adequacy of these procedures. By giving a comparative investigation, this overview points to prepare analysts and specialists with a comprehensive understanding of estimation investigation on Tweets. This information can be saddled for different applications, such as gaging open conclusion on current occasions, observing brand notoriety, or illuminating promoting techniques. Keywords Dataset, Sentiment analysis, Random Forest, Challenges and Future, Stopford’s, NLP etc.

Sentiment analysis of coronavirus data with ensemble and machine learning methods

The coronavirus pandemic has distanced people from social life and increased the use of social media. People's emotions can be determined with text data collected from social media applications. This is used in many fields, especially in commerce. This study aims to predict people's sentiments about the pandemic by applying sentiment analysis to Twitter tweets about the pandemic using single machine learning classifiers (Decision Tree-DT, K-Nearest Neighbor-KNN, Logistic Regression-LR, Naïve Bayes-NB, Random Forest-RF) and ensemble learning methods (Majority Voting (MV), Probabilistic Voting (PV), and Stacking (STCK)). After vectorizing the tweets using two predictive methods, Word2Vec (W2V) and Doc2Vec, and two traditional word representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BOW), classification models built using single machine learning classifiers were compared to models built using ensemble learning methods (MV, PV and STCK) by heterogeneously combining single machine classifier algorithms. Accuracy (ACC), F-measure (F), precision (P), and recall (R) were used as performance measures, with training/test separation rates of 70%-30% and 80%-20%, respectively. Among these models, the ACC of ensemble learning models ranged from 89% to 73%, while the ACC of single classifier models ranged from 60% to 80%. Among the ensemble learning methods, STCK with Doc2Vec text representation/embedding method gave the best ACC result of 89%. According to the experimental results, ensemble models built with heterogeneous machine learning classifier algorithms gave better results than single machine learning classifier algorithms.

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

In the context of democracy in Indonesia, elections play a crucial role, and survey agencies often publish their results on social media. User responses, especially from voters, often express dissatisfaction, including distrust, insults, and negative comments, if the candidate they support receives low survey results. Therefore, this study aims to examine the level of public trust in the survey results of Presidential candidates in 2024 using the Support Vector Machine (SVM) and Logistic Regression algorithms. The study utilized data from 1778 Instagram comments and 985 Twitter tweets. The process involved problem identification, data collection, and system implementation, such as preprocessing, labeling, SMOTE, TF-IDF, data splitting, model classification, and evaluation. The results show that SVM with an 80% training data and 20% test data scenario provides high accuracy, namely 93.19% from Instagram and 91.19% from Twitter. Logistic Regression, with the highest accuracy of 89.79% from Instagram and 88.01% from Twitter in the same scenario. Sentiment analysis using SVM scenario one resulted in 195 positive comments and 216 negative comments. Logistic Regression scenario one shows 180 positive sentiments and 216 negative sentiments. From the classification results, it can be concluded that the level of public trust tends to be negative towards the survey results of the 2024 Presidential candidates, both using SVM and Logistic Regression.

TEXT BASED MASS OPINION MINING USING NEURAL NETWORK ALGORITHM WITH ABSTRACTIVE SUMMARIZATION

Our work, which focuses specifically on Twitter data, presents an advanced methodology for large-scale opinion mining. Sentiment analysis, or opinion mining, is the process of mechanically identifying and categorizing sentiments from textual data. We achieve accurate sentiment categorization and informative result summarizing by combining the latest neural network methods with abstractive summarization techniques. Tokenization, a crucial stage in natural language processing (NLP) activities, is where we start by using BERT (Bidirectional Encoder Representations from Transformers). By capturing the contextual subtleties of language used in Twitter tweets, BERT's contextual embeddings facilitate accurate tokenization. Our model successfully captures the textual data by utilizing BERT's capabilities, which paves the way for further sentiment analysis. We then apply Bidirectional Long Short-Term Memory (BiLSTM) to sentiment categorization. Recurrent neural networks (RNNs) such as BiLSTM are particularly good at recognizing sequential dependencies in data. Sentiment analysis relies heavily on word order since sentiments are frequently influenced by the context that words that come before and after give. Our model's incorporation of BiLSTM allows it to precisely classify tweets as either positive or negative by capturing these sequential dependencies. We also present an abstractive summary element to produce brief summaries that capture dominant attitudes in the dataset. Abstractive summarization generates logical summaries that encapsulate the main ideas of numerous tweets, going beyond simple sentence selection and concatenation. We evaluate the quality of the generated summaries and the accuracy of sentiment categorization achieved by our model through extensive experimentation on a representative and diversified Twitter dataset. Our findings show how reliable and effective our approach is at identifying significant sentiment trends and offering insightful information about the general thoughts shared on Twitter. In the end, our work advances the field of opinion mining and provides useful instruments for deciphering and evaluating massive amounts of social media data.

Accountability of Islamic Non-Governmental Organizations in Indonesia: A Netnographic Study

This study aims to analyze the online public's views on the accountability of the XYZ Foundation (XYZ), an Islamic non-government organization (NGO). This qualitative research uses netnography methods and primary data in the form of Twitter tweets with the trending hashtag #JanganPercayaXYZ. This research was carried out not long after the XYZ case emerged. The study includes an examination of relevant supporting documents, for example, the financial statement of XYZ. Data analysis was conducted in four stages: investigation, interaction, immersion, and integration. The results show that the public's views indicate that XYZ lacks accountability and transparency because the campaign is not in line with reality. There are too many cuts in donations, which has an impact on damaging public trust. XYZ is considered to be using religion and humanity to gain attention and empathy from donors. However, these do not deny XYZ's great contribution and performance in humanitarian affairs. These results have implications for the need to improve Islamic philanthropic institutions' regulation, governance, and accountability, as well as the prudence and selectivity of donors in donating through similar institutions. This study contributes to the literature on accountability in Islamic NGOs by utilizing netnography and data from social media.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

Muslims Acceptance of Lesbian, Gay, Bisexual, and Transgender Issues in Indonesia

This article aims to explore netizen criticism of the existence of lesbian, gay, bisexual and transgender people in Indonesia. LGBT activists have utilised social media to convey messages of equality, leading to pros and cons in Indonesian society. The urgency of this research is to understand how Muslims accept LGBT issues in Indonesia. This research uses a qualitative method with a media analysis approach. The data collected by the author were 10,000 Twitter tweets of netizens regarding LGBT issues in Indonesia, and then this data was re-analysed by drawing actor networks on Gephi software. The results show that the Qur'an gives a description of the people of Prophet Luth who committed homosexual acts and were labelled as people who behaved stupidly. Then, in another verse, LGBT can damage conventional marriage bonds to harm social morals. Then, MUI firmly rejects LGBT through Fatwa No. 57 Year 2014, which generally aims to maintain moral values prevailing in society. Through analysis using Netlytic, there are several dominant actors—the focus of actor discussions is political and social discussions. LGBT is still not recognised by society and the state because this kind of deviant behaviour is not a freedom that can be openly accepted.

Enhancing Twitter Tweet Topic Understanding through Ensemble Learning

Since Twitter’s introduction to social media of the hashtag as a content grouping label in 2007, the symbol and its associated usage as a classification la- bel have seen widespread adoption throughout social media and other platforms. While the content of a post can be conveniently classified using said post’s hash- tags, classifying posts that do not contain hashtags proves to be a much more challenging problem. In this paper, we propose a system for identifying a post’s hashtags using only the non-hashtag terms of the post and, by extension, address the issue of classifying the contents of posts that do not contain hashtags.

Social Media Sentiment Analysis on Waqf and Education

This research aims to measure public sentiment towards waqf education by using primary data derived from Twitter tweets. Sentiment measurement utilizes a Python Library software called VADER (Valence Aware Dictionary and Sentiment Reasoner) to classify the sentiment in each tweet analyzed. The results revealed that India was the location with the highest tweet activity around waqf education, reaching a total of 656 tweets. The highest peak of tweet activity occurred in 2020, with 170 tweets during the study period. In terms of sentiment polarity, the majority of tweets showed positive sentiment (48.3%), followed by neutral sentiment (27.0%), and negative sentiment (24.8%). The importance of these findings lies in the in-depth understanding of how people respond to and perceive waqf education. This sentiment analysis can provide valuable insights for decision makers and relevant stakeholders in developing and improving waqf education practices in the future

The Influence Of Applying Stopword Removal And Smote On Indonesian Sentiment Classification

Information, like public opinions or responses, can be obtained through Twitter tweets. These opinions can expressed as a sentiment. Sentiments can be positive, neutral, or negative. Sentiment analysis (opinion mining) on a text can performed through text classification. This research aims to determine the influence of implementing Stopword Removal and SMOTE on the sentiment classification model for Indonesian tweets. The algorithms used in this research are Logistic Regression and Random Forest. Based on the evaluation, the best classification model in this research was achieved by implementing the Random Forest algorithm along with SMOTE, with an f1-score value of 75.03%. Meanwhile, implementing the Random Forest algorithm and Stopword Removal achieved the worst classification model, with an f1-score value of 68.09%. Implementing Stopword Removal in both algorithms has a negative impact in the form of a decrease in the resulting f1-score. Meanwhile, the performance of SMOTE provides a positive impact in the form of an increase in the resulting f1-score. This happened since Stopword Removal could reduce information and alter the meaning of processed tweets, causing the tweet to lose its sentiment.

Twitter Analysis on Waqf and Health

This study aims to observe the evolution of public sentiment towards waqf health around the world. This research approach uses primary data by taking samples from Twitter tweets during the period 2011-2023. Sentiment analysis was conducted using VADER Python programming to classify sentiment into positive, negative and neutral. The results showed that India was the country with the largest distribution of tweet data discussing the issue of waqf health. Furthermore, positive sentiments dominate the discussion on the Twitter platform, reaching 63.2% of the total sentiments expressed. On the other hand, negative sentiments took second place with a contribution of 21.1%. Although neutral sentiment was the least, it still accounted for 15.8% of the total sentiment expressed in the discussion. Through this approach, this research provides a more in-depth picture of how people's views on waqf health evolve over time. This sentiment data can serve as a basis for further understanding of the global perception of waqf health initiatives

Portal Masjid “Mosque Wanted” Solusi Pencarian Lokasi Masjid, Info Kajian & Berita Seputar Masjid di Yogyakarta

The dissemination of information and news about mosques in the Yogyakarta area has predominantly relied on social media and the Internet. Most mosques, especially the larger ones, have established their own social media accounts to showcase their activities. However, the information shared is typically limited to text and graphics, such as WhatsApp broadcasts, Instagram posts, Facebook updates, Twitter tweets, or posters displayed within the mosque premises. This text and graphic-based approach restricts accessibility for individuals who are unable to attend mosque activities due to various reasons, including difficulties in navigating to the mosque. This research aims to develop a Web-based Geographical Information System (GIS) that provides a mosque search solution, study information, and news about mosques in Yogyakarta. The system utilizes a responsive web design to enhance the dissemination of information, facilitate navigation, and stimulate public interest in studying Islamic sciences in Yogyakarta. The outcomes of this study offer valuable support to mosque administrators in providing comprehensive information about the mosque, particularly for Islamic da'wah activities. By incorporating detailed information and location data into the system, the dissemination of da'wah information can be improved. Moreover, the system enables the community to easily locate nearby mosques that offer Islamic da'wah activities and access real-time information about these activities.

Illocutionary Speech Acts of Assertive, Directive, Expressive, Commissive, and Declarative in the Tweets of Jibran Kholil Jibran Account

This study aimed to describe the form of assertive, directive, expressive, commissive, and declarative illocutionary speech acts analysed based on the data collected. The method used was descriptive qualitative research. The data used were Arabic tweets contained in the twitter account @gbranit جبران خليل جبـران which totalled 30 tweets. Data collection used listening, note-taking, and documentation. The listening and note-taking techniques were carried out by collecting twitter tweets and selecting them for analysis. The analysis technique used Miles and Huberman’s interactive model comprising data collection, data reduction, data exposition, and conclusion drawing. This study revealed 30 data classified into 5 forms of illocutionary speech acts, namely 3 assertive speech acts, 6 directive speech acts, 13 expressive speech acts, 5 commissive speech acts, and 3 declarative speech acts. From all types of illocutionary speech acts, expressive speech acts were mostly used by speakers.

The systemic impact of deplatforming on social media.

Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Our results suggest that increased Gettr use is not associated with a substantial increase in user toxicity over time. In fact, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the far-right, and that the ability to interact with political opponents may be part of Twitter's appeal to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Brasília insurrections, highlighting the risks that poorly regulated social media platforms may pose to democratic life.

Comparative analysis of trending tweets in Twitter during the year 2022 on days of public health significance

The use of social media in healthcare communication has expanded, public dissemination of health-related information is less common. Objective of the study was to compare the number of tweets received by public health campaigns with trending tweet and likes in twitter on those specific days. Recent posts with hashtags of nine general health and five dental public health related campaigns between 01 January 2022 to 31 December 2022 were selected from Twitter based on public health importance. We looked for top trending campaigns that had the most global reach. Descriptive statistical analysis was performed. Among the nine general health campaigns, no likes were received for five campaigns and three campaigns got likes which were greater than the trending campaigns. Among the oral health related tweets, only National tooth brushing day campaign garnered 76000 likes and none of the other oral health related tweet garnered likes. Future Twitter-based health campaigns should emphasize image-based information endorsed by a reputable organization. Further support for this message from well-known twitter users could expand the information's reach.