User Posts Research Articles

Depression Detection via Capsule Networks with Contrastive Learning

Depression detection is a challenging and crucial task in psychological illness diagnosis. Utilizing online user posts to predict whether a user suffers from depression seems an effective and promising direction. However, existing methods suffer from either poor interpretability brought by the black-box models or underwhelming performance caused by the completely separate two-stage model structure. To alleviate these limitations, we propose a novel capsule network integrated with contrastive learning for depression detection (DeCapsNet). The highlights of DeCapsNet can be summarized as follows. First, it extracts symptom capsules from user posts by leveraging meticulously designed symptom descriptions, and then distills them into class-indicative depression capsules. The overall workflow is in an explicit hierarchical reasoning manner and can be well interpreted by the Patient Health Questionnaire-9 (PHQ9), which is one of the most widely adopted questionnaires for depression diagnosis. Second, it integrates with contrastive learning, which can facilitate the embeddings from the same class to be pulled closer, while simultaneously pushing the embeddings from different classes apart. In addition, by adopting the end-to-end training strategy, it does not necessitate additional data annotation, and mitigates the potential adverse effects from the upstream task to the downstream task. Extensive experiments on three widely-used datasets show that in both within-dataset and cross-dataset scenarios our proposed method outperforms other strong baselines significantly.

The New Neighborhood Watch: An Exploratory Study of the Nextdoor App and Crime Narratives

Community members use the Nextdoor App to prevent crime and circulate information when suspicious activity or criminal misconduct is afoot. The Nextdoor App operates like other popular social media platforms, but unlike Facebook and Twitter, it connects citizens based on geography instead of areas of interest. One unique aspect of the app is posting events and perceptions of suspicious behavior and criminal acts. User posts can provide narratives on feelings, incidents, and perceptions of crime in designated neighborhoods. This exploratory study focuses on how community members in an urban Colorado area use the app as a high-tech Neighborhood Watch. A qualitative research approach with a thematic analysis is implemented to examine neighbors’ perceptions of crime events and community safety. The findings depict that citizens engaged in Nextdoor communications are frustrated with petty community incivilities, property crimes, and law enforcement actions. In addition, the results show that lessons in being a capable guardian, possible increases in fear levels, and surveillance activities are important aspects of understanding social media and crime.

SSRI-Net: Subthreads Stance–Rumor Interaction Network for rumor verification

As online rumors have the potential to greatly affect areas such as social order, stock prices, and presidential elections, there is an emerging necessity for the automation of rumor verification. Although the current methods have achieved satisfactory performance, they still suffer from the following problems. First, the current methods simply concatenate the representations of different subthreads in their models, which may result in omitting some important information. Second, although stance information has been considered for the rumor verification task, it has not been fully utilized. To solve the problems, we propose the Subthreads Stance–Rumor Interaction Network (SSRI-Net) model for rumor verification. The proposed SSRI-Net model first introduces the Subthreads Interaction Attention mechanism between different subthreads to capture the interaction information between subthreads for a better understanding on user posts. Moreover, we also design the Stance–Rumor Interaction Network to fully integrate users’ stance information with rumor verification. We have conducted experiments on two public datasets, namely SemEval-2017 and PHEME datasets, for performance evaluation. Our SSRI-Net model outperforms the previous best models by 5.8% and 7.1% in Macro-F1 and Accuracy respectively on the SemEval-2017 dataset. In addition, our SSRI-Net model also outperforms the previous best models by 4.7% and 5.4% in Macro-F1 and Accuracy respectively on the PHEME dataset. The experimental results have shown that our proposed SSRI-Net model has outperformed the baseline models and achieved the state-of-the-art performance for rumor verification.

A Machine Learning Classification Model for Predicting Depression Using Social Media Posts of User

Depression is a prevalent mental health disorder, with social media platforms serving as a rich source of data for identifying and understanding depressive symptoms. This paper presents a machine learning stratification model designed to predict depression using social media data collected from Twitter, Facebook, and Reddit. The model incorporates sentiment analysis, keyword labeling, and stratification techniques to accurately categorize depressive posts. The research evaluates the model's performance using various metrics and compares it against traditional methods for identifying depressive content.

Machine Learning for Predicting Personality using Facebook-Based Posts

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.

Deep temporal modelling of clinical depression through social media text

We describe the development of a model to detect user-level clinical depression based on a user’s temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clinically relevant features, e.g., depression scores and their consequent temporal patterns, as well as user posting activity patterns, e.g., quantifying their “no activity” or “silence.” Furthermore, to evaluate the efficacy of these extracted features, we create three kinds of datasets including a test dataset, from two existing well-known benchmark datasets for user-level depression detection. We then provide accuracy measures based on single features, baseline features and feature ablation tests, at several different levels of temporal granularity. The relevant data distributions and clinical depression detection related settings can be exploited to draw a complete picture of the impact of different features across our created datasets. Finally, we show that, in general, only semantic oriented representation models perform well. However, clinical features may enhance overall performance provided that the training and testing distribution is similar, and there is more data in a user’s timeline. The consequence is that the predictive capability of depression scores increase significantly while used in a more sensitive clinical depression detection settings.

A novel approach to social content recommendation using deep self-organizing maps and hierarchical clustering

Social media platforms generate a large amount of content users create, which requires methods for suggesting relevant content. In current empirical research introduces an approach to improving social content recommendations using the Deep Self Organizing Map (DSOM) algorithm and hierarchical clustering. The study uses a database that includes user posts, comments, likes, shared content, and user profiles. The DSOM algorithm analyzes and organizes the data, while hierarchical clustering enhances performance. By utilizing the insights gathered from this social content database, we can significantly improve the accuracy and relevance of recommendations. This improvement will ultimately increase user engagement and satisfaction on social media platforms. The findings of this research have implications for recommendation systems on social media platforms and strategies related to promoting content and analyzing user behavior.

Research on the detection model of mental illness of online forum users based on convolutional network

Recently, there will be more than 4.62 billion social media users worldwide. A large number of users tend to publish personal emotional dynamics or express opinions on social media. These massive user data provide data support for the development of mental illness detection research and have achieved good results. However, it is difficult for current mental illness detection models to accurately identify key emotional features from a large number of posts issued by users to detect problem users. In view of the fact that the existing models cannot more accurately extract the words with high emotional contribution in the content of user posts, this paper proposes two hierarchical user post feature representation models, named Single-Gated LeakReLU-CNN (SGL-CNN) and Multi-Gated LeakyReLU-CNN (MGL-CNN). We leverage these 2 models to identify users with mental illness in online forums. For all posts published by each user within a certain time span, the model proposed in this paper can identify key emotional features in them and filter out other unimportant information as much as possible. In addition, the addition of gating units in this paper can significantly improve the performance of emotion detection tasks. The experimental results based on the task of RSDD dataset prove that the performance of the model proposed in this paper is superior to that of the existing methods.

Augmented reality fitness games and subjective well-being of players: a mixed methods study from China

ABSTRACT During the COVID-19 pandemic, people's health and well-being have been greatly affected, so the promotion of residents’ well-being has been the goal of many public policies. The rapid development of digital technologies offers hope because of their potential to impact and improve people's health. The purpose of this study is to explore exercise solutions based on augmented reality (AR) technology and the impact of AR fitness games (ARFG) on residents’ subjective well-being. Based on the theory of psychological empowerment, we use a sequence of mixed study method, to study the inspection of the hypothesis. First, the quantitative study used 278 responses collected through an online survey to test the model. In the subsequent qualitative research, we collected and processed qualitative data such as user comments and posts on online platforms, which complemented the quantitative research and provided additional insights. Findings indicate that the satisfaction of basic psychological needs (i.e. autonomy, competence and relatedness) enhances users’ feelings of empowerment, enhances subjective vitality and reduces stress. In addition, technology readiness variables (i.e. optimism and innovativeness) were found to positively moderate the relationship between psychological empowerment and subjective well-being. This research contributed to ARFG literature and provided practical guidance for stakeholders.

Positive social comparison mechanism based on social comparison theory: Independency and interdependency on SNS in younger generation in Korea

AbstractPeople utilize social networking services (SNSs) as a means of self‐expression. Therefore, it is necessary to create a positive environment for users to share such content. In this study, we investigated the social comparisons made by SNS users based on the social comparison theory. Furthermore, we examined the posting tendency according to the social comparisons made by young people on SNS, specifically in terms of luxury products. The study employs the social comparison theory, as well as passive reactions to posts by SNS users, to offer insights into various aspects of SNS usage behavior, ranging from reading and posting to the satisfaction of uploading. We surveyed college students in South Korea to test our hypotheses. The findings reveal that the younger the users engage in positive social comparisons through social media activities, the stronger their tendency to create independent posts. Thus, this study extends the social comparison theory by suggesting the possibility of promoting a positive environment for social comparisons among SNS followers, thereby strengthening the virtuous cycle structure of SNSs, where followers become publishers. In addition, the study findings suggest that SNS operators and marketers will be able to provide practical help in establishing operational strategies aimed at promoting the posts of SNS users and generating positive effects.

HG-PerCon: Cross-view contrastive learning for personality prediction

Personality prediction task not only helps us to better understand personal needs and preferences but also is essential for many fields such as psychology and behavioral economics. Current personality prediction primarily focuses on discovering personality traits through user posts. Additionally, there are also methods that utilize psychological information to uncover certain underlying personality traits. Although significant progress has been made in personality prediction, we believe that current solutions still overlook the long-term sustainability of personality and are constrained by the challenge of capturing consistent personality-related clues across different views in a simple and efficient manner. To this end, we propose HG-PerCon, which utilizes user representations based on historical semantic information and psychological knowledge for cross-view contrastive learning. Specifically, we design a transformer-based module to obtain user representations with long-lasting personality-related information from their historical posts. We leverage a psychological knowledge graph which incorporates language styles to generate user representations guided by psychological knowledge. Additionally, we employ contrastive learning to capture the consistency of user personality-related clues across views. To evaluate the effectiveness of our model, and our approach achieved a reduction of 2%, 4%, and 6% in RMSE compared to the second-best baseline method.

A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach

Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.

Dual-channel early rumor detection based on factual evidence

Social media facilitates people's free expression and communication, but it also provides a platform for the generation and dissemination of rumors. In order to curb the spread of rumors in time and minimize their harm, early rumor detection methods were born and became a research hotspot. Early rumor detection requires a balance between accuracy and timeliness, but most existing methods only focused on accuracy and neglected efficiency. In this regard, we analyze a large number of Internet rumors and found that some rumors have been repeatedly spread after a small amount of modification. Such old rumors can be quickly and easily identified through factual evidence, while other new rumors require more clues and time to crack. Based on this insight, we propose a dual-channel early rumor detection model named ERD-DC (Early Rumor Detection-Dual-Channel), which leverages deep learning algorithms. Specifically, ERD-DC consists of two distinct channels, a fast channel referred to as channel-E (channel-Easy) and a slow channel named channel-D (channel-Difficult). In the ERD-DC, posts are divided into ‘easy-to-identify’ and ‘difficult-to-identify’ based on the availability of relevant evidence online. Subsequently, both types of posts are directed to their respective channels for detection. Channel-E focuses on identifying easy-to-identify rumors through a straightforward and swift post-evidence matching process, primarily aimed at enhancing timeliness. In parallel, channel-D is tasked with uncovering difficult-to-identify rumors, leveraging a more comprehensive analysis of user behaviors and posts transmission structures to elevate overall accuracy. Experimental results on two real datasets demonstrate that ERD-DC outperforms other state-of-the-art models significantly in early rumor detection with an accuracy rate of 84%.

Emotion detection on social media status in Myanmar language

<span lang="EN-US">Many social media emerged and provided services during these years. Most people, especially in Myanmar, use them to express their emotions or moods, learn subjects, sell products, read up-to-date news, and communicate with each other. Emotion detection on social users makes critical tasks in the opinion mining and sentiment analysis. This paper presents the emotion detection system on social media (Facebook) user status or post written in Myanmar (Burmese) language. Before the emotion detection process, the user posts are pre-processed under segmentation, stemming, part-of-speech (POS) tagging, and stop word removal. The system then uses our preconstructed Myanmar word-emotion Lexicon, M-Lexicon, to extract the emotion words from the segmented POS post. The system provides six types of emotion such as surprise, disgust, fear, anger, sadness, and happiness. The system applies naïve Bayes (NB) emotion classifier to examine the emotion in the case of more than two words with different emotion values are extracted. The classifiers also classify the emotion of the users on their posts. The experiment shows that the system can detect 85% accuracy in NB based emotion detection while 86% in recurrent neural network (RNN).</span>

Using word embeddings to analyse audience effects and individual differences in parenting Subreddits

This paper explores how individuals’ language use in gender-specific groups (“mothers” and “fathers”) compares to their interactions when referred to as “parents.” Language adaptation based on the audience is well-documented, yet large-scale studies of naturally-occurring audience effects are rare. To address this, we investigate audience and gender effects in the context of parenting, where gender plays a significant role. We focus on interactions within Reddit, particularly in the parenting Subreddits r/Daddit, r/Mommit, and r/Parenting, which cater to distinct audiences. By analyzing user posts using word embeddings, we measure similarities between user-tokens and word-tokens, also considering differences among high and low self-monitors. Results reveal that in mixed-gender contexts, mothers and fathers exhibit similar behavior in discussing a wide range of topics, while fathers emphasize more on educational and family advice. Single-gender Subreddits see more focused discussions. Mothers in r/Mommit discuss medical care, sleep, potty training, and food, distinguishing themselves. In terms of individual differences, we found that, especially on r/Parenting, high self-monitors tend to conform more to the norms of the Subreddit by discussing more of the topics associated with the Subreddit.

Perceptions of COVID-19 related risks by platform-based couriers: An analysis of user comment threads on Reddit.

The COVID-19 pandemic increased demand for app-based platform-based couriers, creating job opportunities for individuals who have lost income because of COVID-19. Through various stages of lockdown, courier workers (e.g., delivering for Uber Eats, Amazon Flex, and Lyft) provide an essential service. At the same time, this form of work poses risks for exposure to the SARS-CoV-2 virus as these workers are highly mobile and in contact with many individuals. To explore how platform-based couriers discuss risks associated with their work during periods of high (first wave, second wave, third wave/rise in concerns regarding variants) and low risk during the COVID-19 pandemic, 2020-2021. We provide a narrative analysis of user posts (n = 2,866) on Reddit during periods of interest. Our analysis resulted in three central findings. First, we identified changing patterns in discourse as the pandemic went on. Second, we found that the theme of risk prevailed largely in the first wave, with dialogue dominated by tips and asking for advice about how to manage risk. Third, our findings reveal a growing polarization among users during the latter phases of the study. Polarization largely focused on acceptance (or not) of public health measures and the nature of their work as independent contractors and the role/responsibility of courier companies to offer protection. Our study is the first to document risks, from the perspectives of anonymous couriers who may be unwilling to share their honest opinions and thoughts through primary data collection where anonymity is not guaranteed.

A sequential neural recommendation system exploiting BERT and LSTM on social media posts

Tourists share opinions about Points of Interest (POIs) through online posts and social media platforms. Opinion mining is a popular technique for extracting feedback from tourists who visited various places hidden in reviews, which are used in several tourist applications that generally reflect their preference towards POI. On the other hand, a trip schema is difficult for tourists because they must pick up sequential POIs in unknown areas that meet their limitations and preferences. However, most prior trip suggestion methods are suboptimal for several reasons, including that they do not consider valuable user reviews and rely exclusively on left-to-right unidirectional discovery sequence models. This study proposes a Neural Network-Long Short-Term Memory (LSTM) POI recommendation system for calculating user similarity based on opinions and preferences. In addition, it presents a method for discovering sequential trip recommendations with Bidirectional Encoder Representations from Transformer (BERT) using a deep learning method. Furthermore, this neural hybrid framework identifies a list of optimal trip candidates by combining personalized POIs with multifaceted context. Furthermore, this method employs the valuable information contained in user posts and their demographic information on social media to mitigate the well-known cold start issue. In the experimental evaluation based on two datasets, Tripadvisor and Yelp, this hybrid method outperforms other state-of-the-art methods when considering F-Score, nDCG, RMSE, and MAP.

Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.

A Deep Learning Framework Using Data Augmentation for Accuracy Improvement to Analyze Users Posts on Social Media to Find Signs of Mental Illness

Through their posts on social media, users frequently express their feelings. A deep learning model was developed for this study to determine a user’s mental state based on the data they posted. We gathered articles for this purpose from Reddit forums dedicated to mental health. Our suggested model may disorder, anxiety, depression and Schizophrenia by examining and learning posting information published by users. Based on their posts, we think our algorithm can help identify people who could be experiencing mental illness. The consequences of this model, which may be used in combination to other methods to track the mental health of individuals who use internet extensively, are also discussed in this paper.

Finding Qs: Profiling QAnon Supporters on Parler

The social media platform "Parler'' has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim to profile the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold: (1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5% of all users) openly report supporting the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network. (3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can - to a large extent - discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.