Social Network Posts Research Articles

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others. Sentiment analysis is a process of uncovering the subjective information from a text. Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis. Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people’s opinions about certain topics, news, and products. This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users. As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts. Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers. However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text. In addition, very few reviews have been conducted on the research in this area. Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022. Several databases were extensively searched, and 30 articles that met the criteria were included. The selected articles were reviewed based on their approaches, datasets, and evaluation metrics. The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively. Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.

Semantic-Based K-Means Clustering for IMDB Top 100 Movies

Textual documents are growing rapidly through the internet in today’s modern technology era. Electronic structured databases archive offline and online documents, e-mails, webpages, blog and social network posts. Without appropriate ranking and demand clustering when there is classification without any specifics, it is quite difficult to retain and access these documents. K-means is one of the methods that is frequently used for clustering. In terms of determining the proximity of meaning or semantics between data, the distance-based K-means method still has flaws. To get around this issue, semantic similarity can be estimated by measuring the level of similarity between objects in a cluster. This research provides a method for clustering documents based on semantic similarity. The approach is carried out by defining document synopses from the IMDB and Wikipedia databases using the NLTK dictionary, and we provide a semantic-based K-means clustering approach that assesses not only the similarity of the data represented as a vector space model with TFIDF, but also the semantic similarity of the data Precision, recall, and F-measure, we demonstrate how well the semantic-based K-means clustering technique works using experimental findings from the IMDB and Wikipedia top 100 movies datasets.

Posting nature: A critical perspective on analysing cultural ecosystem services on Instagram

In recent years, social media data have been used in the analysis of nature within the concept of cultural ecosystem services (CES). From the viewpoint of CES, social media posts that contain content about nature are read as virtual human–environment interactions and conclusions are drawn on how humans perceive, interact with and value the natural environment. We argue that these analyses often lack a deeper understanding of the inherent mechanisms of social media and the sociocultural dimension of the users who produce this data. In this light, we evaluated social media posts that show images of species and landscapes in the bid to gain an understanding of the cultural processing of nature-based posts in digital social networks. We conducted the study using the popular social media platform Instagram. By analysing 124 profiles and conducting five semi-structured interviews, we examined how Instagram posts that focus on nature (in terms of species and natural landscapes) are portrayed online and how these posts are utilized as a cultural entity for self-presentation purposes. Based on this empirical data, we provide a critical perspective on the use of ‘nature posts’ on Instagram for analysing CES. Our results show that nature-focused Instagram posts are a cultural media performance forming contexts of meaning with which human subjects interact. Furthermore, these posts underlie a perceived affordance where the user’s social media practices are essentially tied back to mechanisms of recognition and have to be understood as self-promotions. Images on Instagram that show species and natural landscapes are being used here as an image resource for the purpose of expressing social distinction. Therefore, we particularly argue for a stronger collaboration between the natural, social and cultural sciences to overcome the difference in terms of understanding, analysing and promoting values of nature and CES.

Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral.

Combating misinformation in times of COVID-19: A comparison of the social network strategies of the Spanish government and the autonomous communities

Social networks offer excellent opportunities for healthcare organizations to disseminate information and communicate with individuals during a health crisis, since they can influence health-related decisions and perspectives. In this paper we compare strategies carried out by the Spanish government and the autonomous community authorities on several social networks (Facebook, Instagram, and Twitter). In spite of offering two-way communications, actual use of such platforms was one-way. In addition, some agencies did not have profiles on these platforms; in fact, two agenices were not present on Instagram, the platform with the youngest users. Finally, there was an increasing use of images on social network postings. Results demonstrate that in times of health crisis, some governmental agencies employ social networks mainly as a tool to disseminate information.

Destination Image Analysis with User-Generated Content: A Computer Vision and Machine Learning Approach

This study proposes an approach to identify the elements that shape the destination image in the minds of potential tourists who peruse social network posts, based on Instagram images of Foça, a touristic district of İzmir, Turkey, in summer 2019. The elements in shared images that contributed to the development of Foça’s destination image were identified with computer vision, while word embedding and machine learning were used for element categorization and clustering, respectively. The study demonstrates the proportion of the elements in the photographs shared by Instagram users in 27 categories and subsequently, without any human intervention, constructs a representation of the elements the destination image was most dependent upon. Categorization showed that the categories that were the most representative of the destination image of Foça were sea and landforms, celestial, arts, events, urban, boating and water sports and constructional, respectively. The majority of destination image research with social network data sets relies on observation, interpretation or survey results. These studies are time-consuming and labour-intensive due to the large size of social network data. However, in this study, social network data can be analysed faster and efficiently with artificial intelligence and machine learning compared to conventional methods. Furthermore, the innovative methodology developed in the study contributes to the current tourism literature by proposing a decision support system for making computer-assisted tourism marketing decisions.

Cascade Prediction With Self-Exciting Point Process and Local User Influence Measurement

With the rise and large-scale applications of social networking service, the prediction of information cascades has attracted extensive attention of researchers. User influence is an important factor affecting the dissemination of posts in online social networks. However, current studies usually take the number of users’ neighbors as their influence, and do not accurately describe the role of participating users in information dissemination. In this paper, a prediction model of information cascades in social networks is established based on the Hawkes process, and the model considers three factors, i.e., post influence, user influence and users’ response time, to describe the occurrence probability of forwarding events. In order to utilize abundant information of local network topology, we present a new method of calculating user influence, combining with semi-local centrality and local clustering coefficients. Then, a regression tree algorithm is used to determine time correction coefficients to reveal dynamic post influence, and the popularity prediction of posts in social networks is realized. Comparison experiments of different models are carried out on real-world datasets to evaluate the effectiveness and prediction performance of the proposed model, and results show that our method outperforms other counterparts.

Alternative Opinions about Vaccination in Religious Discourse

This article examines speech strategies and tactics in religious communication in the context of the coronavirus pandemic. The purpose of the work is to consider the features of the speech behaviour of communicants during the pandemic crisis. The research methodology is based on the theory of speech communication to some extent present in modern works on the study of discourses of various types, the study of the peculiarities of religious communication, communicative/speech strategies, and tactics applied to different speech material. The researcher refers to texts published in Orthodox media and on Orthodox websites: sermons, comments in Orthodox forums, and posts in social networks. As a result, the author reveals the polemical nature of this segment of religious discourse. For instance, it contains neologisms with evaluative connotations, such as цифровой концлагерь (digital concentration camp), псевдопандемия (pseudo-pandemic), коронобесие (corona-frenzy), коронокризис (corona-crisis), ковид-диссиденты (covid-dissidents), антипрививочники (anti-vaccinators), антиваксеры (antivaxers), ковидиоты (covidiots), антивакс-сектанты (anti-vax sectarians), ковидофобы (covidophobes), вакциноборцы (vaccine fighters), вакцина каннибала (cannibal vaccine), вакциноскептики (vaccine sceptics), etc. The author characterises two types of addressees: 1) those denying the existence of the pandemic and/or the need for preventive measures and/or vaccination; 2) those recognising the fact of the pandemic and/or the need for preventive measures and/or vaccination. The former are characterised by a confrontational type of communication, as they choose strategies of discrediting and manipulation by means of accusations, insults, slander, sarcasm, appeals to “authority”, etc. They violate the principles of cooperative communication and show verbal aggression. The various phobias that the communicants of this group demonstrate are due to a misunderstanding of the soteriological and eschatological teachings of the Church. The addressees of the second group use a speech strategy of persuasion through interpretation, explanation, references to their own experience, and the authority of the Church. The communicants of this group obviously aim for conflict-free communication.

SemAGR: semantic method for accurate geolocations reconstruction within extensive urban sites

Accurate geolocation of users’ posts in social networks plays a vital role in a wide area of research devoted to analysing the urban environment based on social media data. It allows us to effectively correlate information about a real urban object with its description in social networks. Information analysis about extensive urban sites is challenging in such studies because publications correlate extremely unevenly with marked geotags relating to such objects. In addition, users of social networks often intentionally indicate incorrect geolocations to increase their publications’ popularity. This article proposes a solution to the problem of accurate geolocation reconstruction for extensive urban sites. It is proposed to use a combined method that enriches the list of initial geolocations of the social network with the help of external sources. The transformer model is then applied to recognise named entities that help to detect mentions of events or locations in user posts. After detection, posts are redefined to the new locations from the extended list according to the semantic similarity calculated between publications and locations.

The role of the media in escalating social tension in the USA

The media are considered to be a pivotal source of creating and reflecting various processes in modern societies. The media’s role in escalating social tension in the USA is essential and will be examined in detail in the article. The work focuses on the language used in the media to create social tension, the phenomenon, caused by worried feelings, no trust, stress, hostility, unfriendliness, not sharing common needs or interests, anxiety, violence, and conflict. The pandemic of COVID-19 has led to inequality of views in terms of health and life-activities and has triggered off a wave of anxiety, stress and other manifestations of tension in different social spheres. The analysis of the mass media, namely publicist articles, blogs, forums, and posts in social networks, allows seeing people’s discontent about the current realities in medicine, education, ecology, and politics. People’s feelings of discontent and stress can be generally conceptualized as social tension.

Beyond barriers — discovering oneself and/or search for freedom

This article considers the individual boundaries of the “possibility” of everyday life. The individual, cultural, and social restrictions that have historically existed and exist to the present are shown. Variants of numerous changes in people’s daily lives, allied to the life beyond the barriers which is associated with their individual lifestyles, their ability to overcome themselves are analyzed. The connection between subculture and the construction of the individual lifestyle beyond the limits of the possibility is revealed. The need for life “above the barriers” can be determined by the search for oneself and the desire for self-realization. This may lead to the need to restructure one’s lifestyle, to go beyond the limits of possible everyday life, for example, in bohemian or transitive everyday life. The positive and negative consequences of such overcoming, motivational and social factors affecting the changes in behavior and communication style, changes associated with the necessity to create life on new bases that do not always correspond to the personal capabilities are revealed. But the desire for personal freedom of self-expression can come across social barriers and preventions, and in that case life beyond the limits of the possibility is determined by the degree of rigidity of external pressure. Variants of such a life “above the barriers” of artists, writers, musicians are analyzed. New options of the “space of the possibility” provided by the digital society are considered, first of all, the features of self-presentations and posts in social networks. It is shown that some properties of social networks, primarily transparency, have an ambivalent meaning for an individual lifestyle in the new digital everyday life. Attention is focused on new options of the possible lifestyle, outside the rigid framework, but not in conflict with the main social norms. The necessity of analyzing the everyday lifestyle over the barriers from the position of harmony between the integrity and variability of the personality is proved.

Transferring recommendations through privacy user models across domains

Although privacy settings are important not only for data privacy, but also to prevent hacking attacks like social engineering that depend on leaked private data, most users do not care about them. Research has tried to help users in setting their privacy settings by using some settings that have already been adapted by the user or individual factors like personality to predict the remaining settings. But in some cases, neither is available. However, the user might have already done privacy settings in another domain, for example, she already adapted the privacy settings on the smartphone, but not on her social network account. In this article, we investigate with the example of four domains (social network posts, location sharing, smartphone app permission settings and data of an intelligent retail store), whether and how precise privacy settings of a domain can be predicted across domains. We performed an exploratory study to examine which privacy settings of the aforementioned domains could be useful, and validated our findings in a validation study. Our results indicate that such an approach works with a prediction precision about 15%–20% better than random and a prediction without input coefficients. We identified clusters of domains that allow model transfer between their members, and discuss which kind of privacy settings (general or context-based) leads to a better prediction accuracy. Based on the results, we would like to conduct user studies to find out whether the prediction precision is perceived by users as a significant improvement over a “one-size-fits-all” solution, where every user is given the same privacy settings.

A Multimodal Deep Framework for Derogatory Social Media Post Identification of a Recognized Person

In today’s era of digitization, social media platforms play a significant role in networking and influencing the perception of the general population. Social network sites have recently been used to carry out harmful attacks against individuals, including political and theological figures, intellectuals, sports and movie stars, and other prominent dignitaries, which may or may not be intentional. However, the exchange of such information across the general population inevitably contributes to social-economic, socio-political turmoil, and even physical violence in society. By classifying the derogatory content of a social media post, this research work helps to eradicate and discourage the upsetting propagation of such hate campaigns. Social networking posts today often include the picture of Memes along with textual remarks and comments, which throw new challenges and opportunities to the research community while identifying the attacks. This article proposes a multimodal deep learning framework by utilizing ensembles of computer vision and natural language processing techniques to train an encapsulated transformer network for handling the classification problem. The proposed framework utilizes the fine-tuned state-of-the-art deep learning-based models (e.g., BERT, Electra) for multilingual text analysis along with face recognition and the optical character recognition model for Meme picture comprehension. For the study, a new Facebook meme-post dataset is created with recorded baseline results. The subject of the created dataset and context of the work is more geared toward multilingual Indian society. The findings demonstrate the efficacy of the proposed method in the identification of social media meme posts featuring derogatory content about a famous/recognized individual.

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy.

218 Online Social Listening Surveillance for Earlier Insights About Loperamide Abuse/Misuse, 2015-2021

Loperamide (LOP) is a safe, effective antidiarrheal medication available over-the-counter (OTC) for over 30 years with a maximum dose of 8 mg/day for adults. Intentional LOP abuse/misuse (ingesting 70-200 mg/day to self-treat opioid withdrawal symptoms or achieve euphoria) was first reported in online forums in 2005. Following a 2016 US Food and Drug Administration (FDA) Drug Safety Communication on the potential risk of cardiovascular adverse events with high doses of LOP, the OTC industry initiated a communications campaign (www.LoperamideSafety.org) to educate health professionals, including emergency medicine specialists, about LOP abuse/misuse awareness and prevention. Retrospective surveillance (eg, FDA Adverse Event Reporting System database and National Poison Data System) has traditionally been employed to track reports of drug abuse/misuse. We utilized modern online social listening technology to observe and analyze comments/conversations (posts) about LOP abuse/misuse on various websites and online social media platforms to gain earlier insight into potential LOP abuse/misuse. Utilizing the software tools Crimson Hexagon and Synthesio, we scanned thousands of publicly accessible user posts in online blogs, forums, and social networks – including major platforms such as Twitter, Facebook, YouTube, Reddit, and Forums – from January 2015 to March 2021 to observe posts about LOP abuse/misuse. Listening consisted of keyword-based scrapes of user-generated content using search terms including loperamide, Imodium, overdose, OD, get high, getting high, withdrawal, and related terms/phrases. Findings were analyzed and reported quarterly. From January 2015 to March 2021, there were 27,213 user posts regarding LOP abuse/misuse among all observed online platforms. Annual post volume peaked in 2016 before declining in 2019/2020, consistent with the pattern of annual intentional exposure cases reported by the American Association of Poison Control Centers. Over 2015-2021, there was an average of 390 posts per month. Since May 2018, Reddit contained the majority of posts (70%), followed by Twitter (18%), YouTube (6%), Forums (4%), Facebook (<1%), and other sites (<1%). Reddit and Forums accounted for an increasing percentage of posts over time with a decrease for all other platforms. Posts primarily addressed the use of high-dose LOP for easing withdrawal symptoms (73%) or seeking euphoria (17%). Posts about risks and dangers of misuse/abuse were 16% of Reddit posts from Q2 2020 to Q1 2021, with a peak of 22% in Q2 2020. Spikes in posts coincided with key events, including journal article publications regarding effects of high doses (2,499 posts in May 2016 and 830 posts in January 2017) and FDA Drug Safety Communications regarding abuse/misuse of LOP (650 posts in June 2016 and 1,400 posts in January 2018). Post volume also appeared to increase in relation to US COVID-19 infection rates in 2020/2021 slightly. Online chatter about LOP abuse/misuse rose from 2015 to a peak in 2016 before declining in 2019/2020, consistent with the pattern of reported annual intentional exposure cases. While not a substitute for epidemiological surveillance, online social media listening is a useful adjunct surveillance strategy for earlier insight into potential LOP abuse/misuse by observing and analyzing online comments/conversations, sentiments and contexts.

UR: SMART\u2013A tool for analyzing social media content

The digital transformation, with its ongoing trend towards electronic business, confronts companies with increasingly growing amounts of data which have to be processed, stored and analyzed. Instant access to the “right” information at the time it is needed is crucial and thus, the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage. In this context, one important field of application is digital marketing, because sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior based on their reviews, complaints as well as posts in online forums or social networks. However, existing tools for the automated analysis of social content often focus on one general approach by either prioritizing the analysis of the posts’ semantics or the analysis of pure numbers (e.g., sum of likes or shares). Hence, this design science research project develops the software tool UR:SMART, which supports the analysis of social media data by combining different kinds of analysis methods. This allows deep insights into users’ needs and opinions and therefore prepares the ground for the further interpretation of the voice. The applicability of UR:SMART is demonstrated at a German financial institution. Furthermore, the usability is evaluated with the help of a SUMI (Software Usability Measurement Inventory) study, which shows the tool’s usefulness to support social media analyses from the users’ perspective.

Sentiment Analysis of Students' Feedback in MOOCs: A Systematic Literature Review.

In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.

IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING

This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.

Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

Game on! A randomised controlled trial evaluation of playable technology in improving body satisfaction and negative affect among adolescents

Playables are mini-games used in digital advertising and may offer a novel and engaging avenue to improve young people’s well-being at scale. This randomised controlled trial evaluated the immediate impact and protective properties of a psychoeducational playable on adolescents’ body satisfaction and negative affect, and engagement in prosocial behaviours, relative to two active control conditions. Girls and boys aged 13–14 years ( N = 6575) were randomised into one of three conditions: body image playable, body image social networking posts (i.e. static images of the body image playable messages; control 1) or an ocean conservation playable (control 2). Both body image micro-interventions significantly improved state body satisfaction and negative affect, relative to the oceanic playable. No condition buffered against the negative effects associated with viewing idealised media images. Developing playables to counteract the potentially harmful effects associated with surrounding digital environments is a promising avenue for mental health.