User Tweets Research Articles

Novel machine learning technique for analyzing personalities in status updates on facebook

The largest and widely used online social network, Facebook keeps extensive records of user activity as it is expressed in a variety of activities, including Facebook likes, status updates, posts, comments, photographs, tags, and shares. A social media user's tweets and other actions make their psychological foundations quite evident. However, making a prediction about this is difficult. This study investigates how to predict the HEXACO Model Personality Scores, which offer a quantitative assessment of users' personality characteristics, using integrated random forest fused extreme gradient boosting (IRF-XGB). Assemble a database of Facebook status updates and the corresponding personality labels. Natural Language Processing (NLP) methods and the Bag of Words (BoW) tool were used to preprocess the obtained data and extract features. In terms of predicting HEXACO model personality traits, the proposed IRF-XGB yields better results. The prediction accuracy results show that the personality prediction method developed with the IRF-XGB classifier is higher than the average baseline for all the feature sets, with a top prediction accuracy of 97%, even when tested using the identical dataset.

Machine Learning Approach to Detect Depression through Social Media Posts

Abstract: Depression is one of the biggest issues which is increasing rapidly day by day. It has always been a topic for various researchers to discover new methods and techniques to overcome it. It is basically a kind of disorder in which people do not feel happy and joy. Since with the increase of the social networking sites people are expressing their feelings on the social media platforms which are helping the various researchers to analyze these posts and find an effective solution. Now a days there are various studies that aim to exploit machine learning techniques for detecting a probable depressed Twitter (Now X) user tweets. Textual data can be collected from the comments on the post, exchanging information etc. Using Facebook, Twitter, and Instagram. There is a huge amount of data which is present on these platforms which can be used for research. The aim of this study is that whether machine learning can be able to effectively detect the various signs of depression by analyzing the user post. This paper is basically on the sentimental analysis using NLP that identifies that the post is positive, negative or neutral.

Twitter Visualitation of Healty Catering Food Promotion Strategy

This analysis aims to determine whether there are differences in marketing strategies and which strategy is superior between the two health catering industries. Intermediary of Twitter as a place to retrieve data, where Twitter is a social media with the fastest spread of news through hashtags and trending topics of a phenomenon that is busy being discussed. They use qualitative methods by utilizing Social Network Analysis (SNA) and are assisted by several supporting tools such as Gephi, Wordji, Notepad++, and Dictionaries to make it easier for researchers to analyze the data obtained so that it is concrete and relevant. From the visualization of Twitter user tweet data, the researcher found that Twitter users discussed Yellowfit Kitchen health catering more than Gorygourmet, as evidenced by a large number of data visualizations obtained. Then on average, customers choose to subscribe to health catering Yellowfit Kitchen with the pure aim of dieting. At the same time, healthy catering Gorygourmet customers subscribe to a more nutritious diet to avoid disease.

Malicious Social Bot Using Twitter Network Analysis in Django

Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system.

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic.

People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread of the novel virus, many people are forced to stay at home, which significantly impacts people's mental health. It is mainly because the pandemic has directly affected the lives of the people who were not allowed to leave home due to strict government restrictions. Researchers must mine the related human-generated data and get insights from it to influence government policies and address people's needs. In this paper, we study social media data to understand how COVID-19 has impacted people's depression. We share a large-scale COVID-19 dataset that can be used to analyze depression. We also have modeled the tweets of depressed and non-depressed users before and after the start of the COVID-19 pandemic. To this end, we developed a new approach based on Hierarchical Convolutional Neural Network (HCN) that extracts fine-grained and relevant content on user historical posts. HCN considers the hierarchical structure of user tweets and contains an attention mechanism that can locate the crucial words and tweets in a user document while also considering the context. Our new approach is capable of detecting depressed users occurring within the COVID-19 time frame. Our results on benchmark datasets show that many non-depressed people became depressed during the COVID-19 pandemic.

User-Specific Loyalty Measure and Prediction Using Deep Neural Network From Twitter Data

Analyzing the social media data posted by users reveals insights about the business such as customers’ sentiment, limitations, improvements in services, users’ expectations, customer loyalty, and satisfaction. Customer satisfaction and loyalty are measured using net promoter score (NPS), which is based on a survey and often lacks the background of customer context. Several recent methods, net brand reputation (NBR) and net sentiment score (NSS), overcome NPS limitations and utilize social media data to gauge customer loyalty. The NBR and NSS also have the limitation that they do not provide the user-level score. In social media, existing user has an influence on a new user, which cannot be estimated in NBR and NSS. The main contribution of this research is developing novel user-and product-level metrics, which measure the influence, satisfaction, and customer loyalty. User sentiment score (USS) measures customer loyalty, overall influence factor (OIF) computes influence factor, effective influence on user (EIU) gives the effective impact on a new user, and satisfaction score (SATS) estimates satisfaction using user tweets. Social data influence score (SDIS) is proposed to measure the combined impact of user satisfaction and influence. We prepare a Twitter dataset of five popular online learning platforms: Coursera, Udemy, Udacity, Khanacademy, and edX, and analyzed our proposed metrics on them. Prediction of USS into loyal and not loyal customers is carried out using Twitter data and dense neural networks (DNNs). The performance of DNN is compared with random forest classifier (RFC), eXtreme gradient boosting (XGB), and support vector classifier (SVC). DNN gave the highest classification of 98.62% on tenfold cross validation.

Machine Learning Based a Comparative Analysis for Detecting Tweets of Earthquake Victims Asking for Help in The 2023 Turkey-Syria Earthquake

Two major earthquakes in Kahramanmaraş on February 6, 2023, 9 hours apart, affected many countries, especially Turkey and Syria. It caused the death and injury of thousands of people. Earthquake survivors shared their help on social media after the earthquake. While people under the rubble shared some posts, some were for living materials. There were also posts unrelated to the earthquake. It is essential to analyze social media shares to plan the process management effectively, save time, and reach the victims as soon as possible. For this reason, about 500 tweets about the 2023 Turkey-Syria earthquake were analyzed in this study. The tweets were classified according to their content as user tweets under debris and user tweets requesting life material. Popular machine learning methods such as DT, kNN, LR, MNB, RF, SVM, and XGBoost were compared in detail. Experimental results showed that RF has over 99% classification accuracy.

Sentiment Analysis of User Satisfaction Towards Sales Promotion of Gojek Application Service Using Support Vector Machine (SVM)


 
 
 Customer satisfaction is a crucial factor for the sustainability of a company, including tech companies like Gojek. This condition exists because customer satisfaction affects the continuation of customers purchasing or reusing the company's services. Promotions or offers are commonly applied strategies by companies to enhance attractiveness among competitors and improve customer satisfaction. Therefore, this research aims to understand customer sentiments towards promotions or offers provided by the Gojek company to develop better promotional strategies by increasing customer satisfaction. The data source for this research comes from user tweets on the social media platform Twitter which has now changed to "X". The method used to determine the sentiment of this data is the support vector machine.
 
 

Sentiment Analysis Model Development on E-Money Service Complaints

Technology provides various conveniences for users in many aspects, such as in the commercial business world with the development of financial technology or fintech. In Indonesia, there are fintech services with the most users, namely OVO and DANA. Various kinds of criticisms and suggestions related to deficiencies and weaknesses in OVO and DANA services were submitted by users via social media Twitter. This study aims to analyze and develop a sentiment identification model for user complaints against OVO and DANA services on Twitter. This study uses the four classification algorithm methods: Random Forest, K-NN, Decision Tree, and Naïve Bayes Classifier. The data from social media Twitter is taken from user tweets regarding criticism and complaints on OVO and DANA services. The data obtained was 5,000 instances consisting of 20% positive and 80% negative reviews. Accuracy values represent the study results with 87% for Naive Bayes, 86% for Decision Tree, 91% for K-NN, and 87% for Random Forest. Based on these results, it can be concluded that testing of sentiment analysis on user complaints on OVO and DANA services using the K-NN algorithm is superior to the other algorithms. This research results in a system that combines the four prediction models. The evaluation shows that the developed model can accurately classify user sentiment.

A global cyber-threat intelligence system with artificial intelligence and convolutional neural network

Global cyber-attacks significantly impact the economy, society, organizations, and individuals. Existing research on cyber-attacks lacks in demonstrating Artificial Intelligence (AI) based analytical solutions for providing country-wide cyber threat intelligence. Cyber strategists at a national level require AI-based decision support systems for deciding a country’s cyber posture or preparedness. This paper proposes an AI-based solution that autonomously collects multidimensional cyber-attack data on social media posts on cyber-related outcry. The proposed system provides critical analytical capability in the cyber-threat spectrum and uses sophisticated AI-based algorithms for anomaly detection, prediction, sentiment analysis, location detection, translation, etc. The proposed system was deployed from 11 October 2022 to 31 October 2022. During these 21 days, the system autonomously collected 30,203 records on cyber threats covering multiple dimensions of cyber-threat. These dimensions included country-wide daily cyber-attack records by ransomware, exploits, web threats, spam, malicious mail, network attacks, local infections, and on-demand scan. Moreover, the system performed AI-based acquisition and analysis of 3789 cyber-related tweets from 3402 tweet users in 37 different languages. The system also autonomously translated 893 non-English tweets. The proposed system is the first solution that uses Convolutional Neural Network (CNN) based anomaly detection to detect abnormalities in cyber-threat spectrum worldwide and predict cyber-attacks automatically. The proposed system was demonstrated to provide evidence-based decisions on global cyber threats in multiple platforms, including iOS, Android, and Windows.

Deep Learning-Based Depression Detection from Social Media: Comparative Evaluation of ML and Transformer Techniques

Detecting depression from user-generated content on social media platforms has garnered significant attention due to its potential for the early identification and monitoring of mental health issues. This paper presents a comprehensive approach for depression detection from user tweets using machine learning techniques. The study utilizes a dataset of 632,000 tweets and employs data preprocessing, feature selection, and model training with logistic regression, Bernoulli Naive Bayes, random forests, DistilBERT, SqueezeBERT, DeBERTA, and RoBERTa models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results indicate that the RoBERTa model achieves the highest accuracy ratio of 0.981 and the highest mean accuracy of 0.97 (across 10 cross-validation folds) in detecting depression from tweets. This research demonstrates the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for mental health analysis. The findings offer valuable insights into the potential for early detection and monitoring of depression using online platforms, contributing to the growing field of mental health analysis based on user-generated content.

Linguistic markers of depression: Insights from english-language tweets before and during the COVID-19 pandemic

Research shows that the COVID-19 outbreak negatively affected people’s mental health with depression cases being the most prevalently reported mental disorder around the world. This motivated studies on the symptoms of depression through social media such as Twitter, with most of them focusing on behavioural changes in depressive individuals. Linguistic changes in tweets of depressed Twitter users however are under-researched. To date, research on linguistic markers of depression has been linked to self-focused attention and negativity bias in depressed individuals while other domains of cognitive theories of depression have remained relatively unexplored in linguistic studies. This gap in the literature is the motivation for the current paper on linguistic markers of depression in tweets during the pandemic. It focuses on sensitive markers of depression namely first-person singular pronouns, negative emotion words, and absolutist words as they reflect increased self-focus, negativity, and absolutist thinking in depressed individuals. The current study analyses the change in the use of these linguistic markers among depressed Twitter users based on tweets in English one year prior to and a year into COVID-19 pandemic. It also explores emerging linguistic markers of depression beyond the scope of self-focused attention, negativity bias, and absolutist thinking providing understanding of how depression manifests in language during global crises such as the pandemic.

Latent Dirichlet Allocation Utilization as a Text Mining Method to Elaborate Learning Effectiveness

Learning method is a way to explain the lesson materials to students so that the learning process can occur in students as an effort to achieve the goals. Learning methods can be said to be a success if students are active, both physically, mentally, and socially in the learning process, in addition to showing high enthusiasm for learning and having self-confidence. The purpose of this study is to classify the opinions of Indonesian students regarding the existing learning methods and what learning methods they expected. In order to evaluate existing learning methods using the latent dirichlet allocation method. The data used comes from tweets of Twitter users within the range of January to March 2022. The data is taken using the scrapping method through the help of the python twisel library and totaled to 3778 data, then preprocessed through the nltk and Sastrawi libraries. The results of this analysis stated that student opinions can be classified into 3 major topics which state students' opinions regarding effective learning methods, student difficulties in applicable learning methods, and high cross-departmental interest.

TWITAUTOLOGY: NEOLOGISM IN K-POP FANBASE ON TWITTER

Every fandom has a unique pattern of language usage to communicate with one another. Different fandoms, of course, affect their language patterns respectively, because the neologisms that are formed can represent the identity of each fandom. K-Pop fandom itself has a wide community all over the world that is connected through a virtual network. Twitter is a virtual data network platform used by many members of the K-Pop fandom. To find out about at what level neologism in K-Pop fandom on Twitter is formed, the data are collected by sampling tweets of Twitter users who are part of three K-Pop boy group fandom namely Seventeen, Stray Kids and Treasure, who use Vocabulary, Orthography, Grammar and Pragmatic in their tweets using descriptive qualitative methods. The findings reveal that some language aspects in K-Pop fandom share different knowledge from the regular language. The results show that the K-Pop fandom community tend to create neologisms based on the identity of the specific idol they like, not only within the K-Pop fandom itself, which makes it unique. This research provides insight that neologism exists in the K-Pop fandom community on twitter to provide the community members an easier way to communicate without having to explain the context.

PREDICTING MARKET SEGMENTS FROM TWITTER DATA USING ARIMA TIME SERIES ANALYSIS

Twitter data on social media can be used to predict potential market segments in the future. The continuous nature of Twitter data and data collection sequentially over a certain period uses a text mining process with time series data that matches the actual data. The problem to be solved is to forecast market segments using Twitter data with greater accuracy. Various market segments that are of business interest through the tweets of individual Twitter users have not been utilized optimally. Based on the Twitter data pattern, the research shows that the data pattern is not stationary, so to analyze the data, it is necessary to use the Autoregressive Integrated Moving Average (ARIMA) method. This study aims to analyze time series data from Twitter data and predict market segment predictions using the ARIMA method. The ARIMA method is a method that has advantages in flexibility, the ability to handle stationary and non-stationary data, as well as short-term forecasting with a statistical approach in various time series data forecasting applications. Prediction results using the ARIMA method with an accuracy rate of 94.88%. There are several ways to measure model validation including MAD, MSE, RMSE, F1-Score, and MAPE. In this study, MAD, MSE, and MAPE were used with an accuracy rate of 5.22%. This study succeeded in applying the ARIMA method to time series data from Twitter to forecast market segments with high accuracy, opening opportunities for utilizing Twitter data in business strategy.

Behaviour and Bot Analysis on Online Social Networks

The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefore, separating malicious behaviors from legitimate ones is crucial. This research aims to evaluate Botometer and RepScope systems to analyze the temporal posting behaviors of Twitter, Reddit, and Parler users and to identify bots, trolls, and malicious behaviors.

User depression and severity level prediction during COVID-19 epidemic from social network data

In recent times, depression becomes a major issue, which causes suicide, particularly among youngsters. During the Coronavirus disease (COVID-19) epidemic, many organizations suggested social distance and quarantine actions, which cause major attention to the mental health and depression of each individual. Most population express their emotions by using modern social media technologies like Twitter, Facebook, etc. By considering user tweets, a Long Short-Term Memory (LSTM)-based classifier was designed that learns rich attributes such as psychological, contextual, cognitive, person-level and distress-dependent n-gram attributes of each user for depression severity level prediction. But, it did not learn the social network structural properties of the most prominent communities, which influences the prediction outcomes. Hence in this article, a novel model is developed to predict the user’s depression severity levels by considering the social network structure of most prominent communities and influence measures. At first, it analyses the physical characteristics of the most prominent groups based on their balanced local and global power distribution. Then, the influential users and communities are identified along with the rich group of attributes. Moreover, those attributes are provided to the LSTM classifier for the user’s depression severity level prediction during the COVID-19 epidemic. Finally, the investigational outcomes exhibit that the presented model attains 93.53% accuracy and 0.4376 Root Mean Square Error (RMSE) contrasted with the conventional classifiers to estimate the user’s depression severity level.

Detecting bots in social-networks using node and structural embeddings

Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot accounts have malicious intent, identifying bot accounts in general is an important and difficult task. In the literature there are three distinct types of feature sets one could use for building machine learning models for classifying bot accounts. These feature-sets are: user profile metadata, natural language features (NLP) extracted from user tweets and finally features extracted from the the underlying social network. Profile metadata and NLP features are typically explored in detail in the bot-detection literature. At the same time less attention has been given to the predictive power of features that can be extracted from the underlying network structure. To fill this gap we explore and compare two classes of embedding algorithms that can be used to take advantage of information that network structure provides. The first class are classical embedding techniques, which focus on learning proximity information. The second class are structural embedding algorithms, which capture the local structure of node neighbourhood. We show that features created using structural embeddings have higher predictive power when it comes to bot detection. This supports the hypothesis that the local social network formed around bot accounts on Twitter contains valuable information that can be used to identify bot accounts.

Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering

Over the past few years, social media has become essential for getting up-to-date information and interacting online. During presidential elections in Indonesia, Twitter has grown as a crucial platform for expressing opinions and sharing information. This study focuses on analyzing the content types and emotions of tweets related to Anies Baswedan, one of the presidential candidates. The results show a variety of discussions, including support, criticism, and discussion of policies for the 2024 presidential candidate. Clustering enables meaningful information extraction from vast Twitter data. Data were clustered using Agglomerative Hierarchical Clustering, which resulted in the identification of 10 clusters. With 4 clusters containing opinion content and 6 clusters containing information content. In addition, 6 clusters reflect excitement, 3 reflect expectations, and 1 reflect doubt. This research provides insights into the Twitter conversation around the 2024 presidential election, providing an understanding of content and emotions expressed by users.

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

AbstractDepression is a severe mental health issue. The user‐generated content on social media (SM) is growing nowadays. Some computational approaches have been proposed for detecting depression based on users' SM data. However, because of the use of formal language, short range of words and misspellings in the SM data, depression detection (DD) is a challenging task. This paper proposes a novel deep learning (DL) technique for performing DD of the SM data with the help of the hybrid feature selection (FS) mechanism. Initially, two publicly available datasets containing user tweets are collected for implementing the proposed research model. Then the collected datasets are preprocessed for further processing. The preprocessing phase includes critical processes that contribute to creating a ready‐to‐use dataset for training and testing. After preprocessing, the preprocessed data is divided into prime and non‐prime words based on the dictionary approach. After that, the hybrid FS approach is implemented to select the most relevant features from the prime and non‐prime words for higher classification accuracy (AC). In the hybrid model, firstly Term Frequency Inverse Document Frequency integrated Modified Information Gain (TFIDF‐MIG) approach is proposed that assigns the score value of each prime and non‐prime word in the dataset. Secondly, optimal features are selected from the weighted features using the Improved Elephant Herding Algorithm (IEHA). Finally, the decided features from the hybrid model are fed into the DL model, namely attention included improved ReLU‐based Convolution Neural Network with Long Short‐Term Memory (AIRCNN‐LSTM) for DD. Experiments are performed on the collected datasets to assess the proposed model's performance efficiency. The results of the extensive experiments show that the presented work outperforms existing techniques regarding DD classification AC by locating the best solutions. At the same time, it reduces the number of features chosen.