User Posts Research Articles

DESIGN AND IMPLEMENTATION OF ECOMMERCE WEB APPLICATION USING MERN STACK AND DATA SCIENCE

: Social media has been influencing e-commerce in multiple ways by being a platform of marketing for ecommerce businesses over the years. This project boosts the idea of social commerce by providing a platform for small businesses to grow and reach the world. At the same time it provides a platform for the internet community to explore various products. A web application where users can register themselves in two ways, as a seller or as a customer. Sellers can post their products and will get insights on how their business is growing in the e-market. A detailed analysis will be provided based on the orders, no. of visitors, likes, etc. On the other hand, the customers can view the products as well as the posts just like on any other social media platform. These users can follow other users and see the posts of other users as well as they can search for a particular product in two ways, text and visual. Results will contain the products and the posts. Also, recommendations will be shown to the user. Users can follow, like and buy products from sellers. Features such as personalized recommendation, product classification, post engagement help the small businesses to create a brand. Being a fusion of social media and e-commerce, this web app provides a single platform for small businesses and social influencers.

The Role of the Virtual Social Network (Facebook) in Spreading Verbal Abuse: A Perspective of Jordanian University Students

Objectives: This study aimed to investigate and analyze the role of the virtual social network, specifically Facebook, in spreading verbal abuse among Jordanian university students. Additionally, the study aimed to identify the concept and types of "verbal abuse" prevalent on Facebook, as perceived by Jordanian university students.
 Methods: The study employed a descriptive analytical approach to achieve its objectives. Data was collected through a questionnaire distributed to 371 male and female students in public universities.
 Results: The results revealed that the students believe Facebook contributes to the spread of verbal abuse, evident in the language used in users' tweets and posts. Moreover, the study found no statistically significant differences in the role of Facebook in spreading verbal abuse among the study sample based on gender or educational level.
 Conclusions: Based on these findings, the study recommends conducting further research on the role of Facebook in perpetuating verbal abuse.

Social Media in Stroke Career Development.

Social Media in Stroke Career Development.

IMPLEMENTATION OF THE INTELLECTUAL SYSTEM OF SENTIMENT ANALYSIS AND CLUSTERIZATION OF PUBLICATIONS IN THE TWITTER SOCIAL NETWORK

Thanks to the intensive development of social networks, the intensity of exchange of short electronic text messages is constantly increasing, the tone of which can serve as a sensitive indicator of public mood and important social phenomena, interesting for sociologists, politicians, economists, and specialists in other fields. In this regard, the task of automating the processing of such natural language messages is of significant scientific and practical interest. The object of this study is the sentiment of user publications in the Twitter social network. Due to the great popularity of the social network itself and the large number of user messages, which are short in nature, it is possible to conveniently determine the mood of user posts and combine them into clusters according to the given parameters of the intelligent system. The subject of the study is methods and algorithms for analysing the sentiment of large arrays of messages containing the necessary keywords and relating to a certain specific topic, determining the factors and distributions of the sentiment of messages based on the input array of system data, dividing messages into main groups and providing estimates within certain defined limits in to each group, division into clusters according to the obtained search point and display of the obtained results in the desired format. The purpose of the work is to implement an intelligent system of sentiment analysis and clustering of publications based on a recurrent neural network of long short-term memory (LSTM) and the k-means clustering algorithm. The following main tasks are solved in the work: 1. To analyse the most used and newest algorithms, methods, approaches and means of implementing tasks of sentiment analysis and clustering of publications in social networks. 2. To develop a conceptual structure of an intellectual system of sentiment analysis and clustering of publications. 3. To form functional tasks for the key modules of the created intelligent system of sentiment analysis and clustering of publications in the Twitter social network. 4. Implement an intelligent system of sentiment analysis and clustering of publications based on a recurrent neural network and the k-means clustering algorithm and conduct experimental verification. Among the methods used for this purpose are the recurrent neural network of long short-term memory; k-means clustering algorithm. The following results were obtained: the general structure of the intellectual system of sentiment analysis and clustering of publications was analyzed, designed and built. The main task of creating the system, first of all, was to improve the recurrent neural network of long-short-term memory, which, thanks to the improved algorithm, significantly facilitates text processing by natural language processors according to text data of a certain size. Also, a special clustering algorithm, namely k-means, was used in parallel, thanks to which it was possible to change the general approach to clustering and the creation of final clusters, in accordance with the obtained results of the work of the recurrent neural network. Conclusions: As a result of applying a combination of LSTM neural network and k-means clustering algorithm, it was possible to speed up the process of sentiment analysis and clustering of posts in the Twitter social network by 10...15% compared to similar convolutional neural networks and hierarchical clustering.

Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter

ABSTRACT Purpose The primary objective of this study was to identify patterns in users’ naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness. Material and Method Data for this study were collected from Reddit and Twitter (2009–2020, n = 85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals. Unsupervised and supervised machine learning models were used to analyze the text data. Results Results suggested 50 topics in reddit finance and 40 each in reddit mental health communities and Twitter. Statistically significant associations were found between mental illness statuses and sentiments and emotions. Posts expressing mental illness showed more negative sentiments and were more likely to express sadness and fear. Discussion and Conclusion Patterns in social media discussions indicate both academic and non-academic consequences of having student debt, including users’ desire to know more about their debts. Interventions should address the skill and information gaps between what is desired by the borrowers and what is offered to them in understanding and managing their debts. Cognitive burden created by student debts manifest itself on social media and can be used as an important marker to develop a nuanced understanding of people’s expressions on a variety of socioeconomic issues. Higher volumes of negative sentiments and emotions of sadness, fear, and anger warrant immediate attention of policymakers and practitioners to reduce the cognitive burden of student debts.

KLASIFIKASI KEPRIBADIAN MENGGUNAKAN ALGORITMA MACHINE LEARNING

Myers-Briggs Personality Type (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This study uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset, the model tested is several machine learning classification models with the help of imlearn under-over sampling techniques for classifying MBTI personality types. This study aims to classify the Myers-Briggs Type Indicator (MBTI) personality type based on text from user posts on the social media platform Reddit. The dataset used in this study consists of around 8,000 posts collected from the MBTI subreddit. Several text processing methods such as tokenization, punctuation removal, and stemming are used to process the raw data before it is entered into the model. The experimental results show that the LSTM model using Adam's optimizer and a learning rate of 0.01 produces good performance with an accuracy of 80.73 compared to other machine learning models. In addition to the LSTM model, XG Boost is also a classification model with the highest accuracy based on 16 personality types producing an accuracy of 60.09 and Logistic Regression with the NS dimension as the best accuracy value of 87.21%.

Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques

Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed.

Predicting the results of the 16-factor R. Cattell test based on the analysis of text posts of social network users

We investigated the possibility of automating the prediction of the 16-factor personality traits by R. Cattell from text posts of social media users. The proposed new method of automating the evaluation of R. Kettell’s 16-factor personality test traits includes language models and neural networks. Implementation of the method involves several steps. At the first step text posts are extracted from user accounts of social media, pre-processed with language model RuBERT and previously trained over a full-connected neural network. The result of this step is a normalized empirical distribution of the posts by the previously introduced classes for each user. Subsequently, based on the distribution of user posts the evaluation of the expression of psychological features of the user is made with the help of support vector machine, random forest and Naive Bayesian classifier. The final data set for model building and further testing their performance was made up of 183 respondents who took the R. Cattell test, with links to their public social media accounts. Classifiers predicting results for six factors (A, B, F, I, N, Q1) of R. Cattells 16-factor personality test were constructed. The results can be used to create a prototype of automated system for predicting the severity of psychological features of social media users. Results of work are useful in the applied and research systems connected with marketing, psychology and sociology, and also in the field of protection of users from social engineering attacks.

627 Trends and Patterns of Neurosurgery Influencers on Instagram: Increasing a Neurosurgeon's Social Media Footprint

INTRODUCTION: Neurosurgery is a highly technical field and Instagram provides the dissemination of information to a large audience. Misleading information can potentially be harmful. METHODS: A social media tracking tool (Influencity, Madrid, Spain) was used to compile a list of the top neurosurgeon Instagram profiles based on number of followers, average interaction, and engagement obtained between May 2022 and June 2022. Profiles that were professional groups/societies/academic centers, not in the United States, had less than 1000 followers, and those not belonging to board-certified neurological surgeons were excluded. Individual posts were categorized as (1) clinical or professional, (2) lifestyle, or (3) patient-centered. Data such as number of followers, likes, comments, hashtags, caption words, user engagement, average user activity, interests, posts, and Instagram verification were collected and analyzed. RESULTS: A total of 84 neurological surgeon “influencer” Instagram profiles were identified. Of the most recent 30 posts for each influencer, clinical and professional posts made up 61.60%, lifestyle posts made up 29.41%, and patient posts made up 1.77%. The most frequent hashtags were #neurosurgery, #medicine, and #surgery. When comparing the 30 profiles with the most followers to the rest of the field, average interaction (437.13 vs. 136.44; p < 0.001), uploads (569.43 vs 199.65; p = 0.003), likes given in the top posts (791.16 vs 217.78; p < 0.001), and comments given in the top posts (32.67 vs 17.19; p = 0.03) were significantly higher in the top 30. Although the top 30 profiles had an average increased posts per week (2.15 vs. 1.34; p = 0.18) posts per month (8.06 vs. 4.87; p = 0.16), and lifestyle posts (3.9 vs. 2.83), those were not significant. CONCLUSIONS: Neurosurgeons may be able to increase their footprint and reach a wider audience by increasing their upload rate. Our analyses found that most neurosurgeons' posts are centered around clinical and professional interests.

Türk Toplumunun Sosyo-Politik Yapısının Gelişim Sürecinde ve Azerbaycan-Türkiye İlişkilerinde Sosyal Medyanın Rolü

Social Networks play an effective role in setting the agenda, making social and political decisions, bringing about changes in the governance system and in many other sectors in Turkey. In 2013, social media was used as the main means of communication to spread the events that started in Taksim Gezi Park of Istanbul to the whole country. President Erdogan of Turkey, invited the citizens to go to the streets on social media to prevent a military coup attempt, that changed the fate of a society on June 15, 2016. Social media, at the same time, comes to the fore in terms of cross-country relations to the agenda of citizens. For example, it is possible to clearly see the reflection of historical friendship and fraternal relations between Azerbaijan and Turkey in the posts of social media users. In particular, the support provided at the state level during the Second Karabakh War by ordinary citizens on the agenda through social media has taken on special significance at a time when information warfare is seen as one of the most important elements of winning the war. The study clarifies the role of social media in communication, evaluates the prospects for the development of Turkish-Azerbaijani relations, and studies the role of social media in the formation of Turkish society and the development of Turkish-Azerbaijani relations using specific examples.

Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.

Using Social Media to Examine Themes Surrounding Fentanyl Misuse and Risk Indicators

Background: Opioid misuse is a crisis in the United States, and synthetic opioids such as fentanyl pose risks for overdose and mortality. Individuals who misuse substances commonly seek information and support online due to stigma and legal concerns, and this online networking may provide insight for substance misuse prevention and treatment. We aimed to characterize topics in substance-misuse related discourse among members of an online fentanyl community. Method: We investigated posts on a fentanyl-specific forum on the platform Reddit to identify emergent substance misuse-related themes potentially indicative of heightened risk for overdose and other adverse health outcomes. We analyzed 27 posts and 338 comments with a qualitative codebook established using a subset of user posts via inductive and deductive methods. Posts and comments were independently reviewed by two coders with a third coder resolving discrepancies. The top 200 subreddits with the most activity by r/fentanyl members were also inductively analyzed to understand interests of r/fentanyl users. Results: Functional/quality of life impairments due to substance misuse (29%) was the most commonly occurring theme, followed by polysubstance use (27%) and tolerance/dependence/withdrawal (20%). Additional themes included drug identification with photos, substances cut with other drugs, injection drugs, and past overdoses. Media-focused subreddits and other drug focused communities were among the communities most often followed by r/fentanyl users. Conclusion: Themes closely align with DSM-V substance use disorder symptoms for fentanyl and other substances. High involvement in media-focused subreddits and other substance-misuse-related communities suggests digital platforms as acceptable for overdose prevention and recovery support interventions.

The ‘magic of filter’ effect: Examining value co-destruction of social media photos in destination marketing

Excessive filter processing of social media photos may cause viewers to question the authenticity of the photos. From the value co-destruction perspective, this research examines the effect of photo filtering on consumer perceptions. Study 1 ran content analysis of 2035 social media user posts and identified that destination marketing failure caused by filtered photos is a process involving tourists' negative emotions and multi-stakeholder (including platform, travel blogger, and destination) value co-destruction, represented by a chain relationship mechanism of “stimulation of filtered photos→ negative emotions of tourists→ failure of destination marketing.” Study 2 applied an experimental design and found that filtered photos have a significant effect on tourists' negative emotions, which play a complete mediating role in the relationship between filtered photos and value co-destruction of destination marketing. Additionally, the moderating effect of tourists’ aesthetic and authenticity pursuit in the influence mechanism was partially verified. Theoretical and practical implications are discussed.

Research on the Display of Female Elements by Chinese Online Game Female Players on the Social Media Platform Xiaohongshu

With the progress of science and technology, games have become a common entertainment item. Traditionally, the vast majority of games have been designed for men. However, in recent years, women are no longer a marginal group among the game audience. They are active in different game communities, especially Xiaohongshu platform. Compared with other game communities, the posts of female users on Xiaohongshu show different characteristics and tendencies. This research first uses the participant observation method to select the posts of three games and the search results of the keyword “online games”. Through analysis, it is concluded that the content of Xiaohongshu game community is mostly casual games, and users are used to combining their personal life with games when creating and sharing contents. However, this also exposed some disadvantages including one-sided content and imperfect system and search mechanism, which requires Xiaohongshu to develop more complete measures and create more activities to enrich the content of the platform. This research is aimed at making the design of game manufacturers and the development of game communities more meet the needs of female consumers by studying the content shared by female players in Xiaohongshu game community, emphasizing the status of women in the game field, and fill the gap of feminism about games.

Predicting Customer Satisfaction through Sentiment Analysis on Online Review

User-generated content, such as user reviews, posts, tags, ratings, and opinions on the internet, can be used as a business indicator if collected and appropriately analyzed. One of the examples is predicting customer satisfaction through implementing big data analytics on online reviews. In analyzing the user-generated content to predict customer satisfaction, the author implements machine learning approach using the Sentiment Analysis method. Five-fold cross-validation was performed to train the classification model. The training was performed with a combination of tokenization methods: term frequency-inverse document frequency (tf-idf) and bag-of-words; n-gram types: unigram, bigram, trigram, and combination of unigram, bigram, and trigram; and machine learning algorithms: linear support vector classification (LinearSVC) and multinomial naïve bayes (MultinomialNB). The result was then evaluated using classification performance metrics such as precision, recall, F1 measure, and AUC score. The result shows that the tf-idf vectorizer performs similarly to the bag-of-words method. A similar result was also observed for machine learning algorithm selection. Both MultinomialNB and LinearSVC produce the same performance. Low-level n-grams (such as unigrams and bigrams) tended to have higher precision, recall, F1 measure, and AUC score than high-order n-grams (such as trigrams). The best results were achieved by combining unigrams, bigrams, and trigrams, resulting in an average performance score of 0.94 for all measurements. From the result and analysis, the author finds that predicting customer satisfaction using text and sentiment analysis methods on user-generated content is possible. The model’s performance in this experiment is decent, with high precision, recall, F1, and AUC score.

Emotional sentiment analysis of social media content for mental health safety

Text sentiment analysis is mostly used for the assessment of the author’s mood depending on the context. The purpose of sentiment analysis is to discover the exactness of the underlying emotion in a given situation. It has been applied to various fields, including stock market predictions, social media data on product evaluations, psychology, the judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked on these topics and generated important insights. These outcomes are useful in the field because they (outcomes) help people comprehend the general summary quickly. Additionally, sentiment analysis aids in limiting the harmful effects of some posts on various social media sites such as Facebook and Twitter. For these reasons and more, we are proposing an approach to filter the social media content that could be emotionally harmful to the user, through getting the social networks content; for that, we have used Twitter API to get the user posts, and then, we have used API natural understanding language API tool to extract and classify the emotions of the Twitter content into five basic emotional categories—Joy, sadness, anger, fear, disgust—into an array of emotions; after that, we have defined a perfect emotion array from over 450 words from the English language. The main purpose of this comprehensive research article is to examine the proposed solution that we have conducted to improve the quality of content displayed to users emotionally.

Don’t Be a Victim During a Pandemic! Analysing Security and Privacy Threats in Twitter During COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users’ security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries’ strategies to instigate malicious activities change with the country’s pandemic situation.

ClarTM: a method for geolocations clarification within extensive urban sites using topic modelling

Determining the correct geolocation of user posts in social networks plays a vital role in many studies investigating and improving the urban environment. We started our research on the possibilities of refining geolocations in extended urban objects, where posts refer to locations exceptionally unevenly, and we continue it in this work, supplementing the location redefinition algorithms with the new methods. Namely, we have developed a three-stage location redefinition algorithm. In the first stage, it expands geolocations from external sources, enriches them with descriptions and combines geolocations from different sources. The second stage redefines posts to locations based on recognising named entities and semantic similarity metrics. And in the third stage, it eliminates inaccuracies in popular geolocations using topic modelling and improves the quality of geolocation clarification by redefining posts to more appropriate geolocations. Topic modelling, which found topics to redefine in 7 (11% of all) locations, increased the redefinition's accuracy by 30% (from 57% to 87%).

Coordinating Narratives Framework for cross-platform analysis in the 2021 US Capitol riots.

Coordinated disinformation campaigns are used to influence social media users, potentially leading to offline violence. In this study, we introduce a general methodology to uncover coordinated messaging through an analysis of user posts on Parler. The proposed Coordinating Narratives Framework constructs a user-to-user coordination graph, which is induced by a user-to-text graph and a text-to-text similarity graph. The text-to-text graph is constructed based on the textual similarity of Parler and Twitter posts. We study three influential groups of users in the 6 January 2020 Capitol riots and detect networks of coordinated user clusters that post similar textual content in support of disinformation narratives related to the U.S. 2020 elections. We further extend our methodology to Twitter tweets to identify authors that share the same disinformation messaging as the aforementioned Parler user groups.

A novel TCNN-Bi-LSTM deep learning model for predicting sentiments of tweets about COVID-19 vaccines.

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.