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Liu Z, Wu Y, Zhang H, Li G, Ding Z, Hu B. Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models. IEEE J Biomed Health Inform 2024; 28:4925-4936. [PMID: 38656850 DOI: 10.1109/jbhi.2024.3393244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Text content analysis for depression detection using machine learning techniques has become a prominent area of research. However, previous studies focused mainly on analyzing the textual content, neglecting the fundamental factors driving text generation. Consequently, existing models face the challenge of poor generalization to out-of-domain data as they struggle to capture the crucial features of depression. To address this, we propose a novel computational perspective of "stimulus-response patterns" that brings us closer to the essence of clinical diagnosis of depression. Adopting this computational perspective allows us to conceptually unify diverse datasets and generalize this perspective to common datasets in the field. We introduce the Stimulus-Response Patterns-aware Network (SRP-Net) as an exemplary approach within this computational perspective. To assess the performance of the SRP-Net, we constructed a multi-stimulus dataset and conducted experimental evaluations, demonstrating its exceptional cross-stimulus generalizability. Furthermore, we demonstrated the promising performance of SPR-Net in real medical scenarios and conducted an interpretability analysis of the stimulus-response patterns. Our research investigates the critical role of stimulus-response patterns in enhancing the generalizability of text-based depression detection models, which can potentially facilitate data-driven depression detection to approach the diagnostic accuracy of psychiatrists.
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Choi GE, Pyun M, Yoon SH, Kim Y, Shin H, Lee SY. Exploring the relationship between YouTube video characteristics and a viewer's mental health traits among young adults. Front Psychiatry 2024; 15:1364930. [PMID: 39035603 PMCID: PMC11258633 DOI: 10.3389/fpsyt.2024.1364930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024] Open
Abstract
We investigated the relationship between individuals' mental health traits and the characteristics of YouTube videos they watch. The mental health traits considered were stress, depression, anxiety, and self-esteem, which were measured using a survey questionnaire. We considered violence shown in a video, brightness and saturation of a video as video characteristics. We utilized the viewing history log data of the participants and analyzed the videos they watched on YouTube using computer vision techniques based on deep learning algorithms. The results revealed that viewers' consumption of violent videos was positively related to stress, depression, and anxiety, but negatively related to self-esteem. Individuals with higher levels of stress, depression, or anxiety tended to view darker videos than those with lower levels of stress, depression, or anxiety.
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Affiliation(s)
- Go Eun Choi
- Department of Digital Analytics, Yonsei University, Seoul, Republic of Korea
| | - Miran Pyun
- Department of Communication, Yonsei University, Seoul, Republic of Korea
| | - So-Hee Yoon
- Department of Communication, Yonsei University, Seoul, Republic of Korea
| | - Yeongchae Kim
- Department of Digital Analytics, Yonsei University, Seoul, Republic of Korea
| | - Hyejin Shin
- Department of Communication, Yonsei University, Seoul, Republic of Korea
| | - Sang Yup Lee
- Department of Communication, Yonsei University, Seoul, Republic of Korea
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3
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Xu X, An F, Wu S, Wang H, Kang Q, Wang Y, Zhu T, Zhang B, Huang W, Liu X, Wang X. Affective norms for 501 Chinese words from three emotional dimensions rated by depressive disorder patients. Front Psychiatry 2024; 15:1309501. [PMID: 38469031 PMCID: PMC10925686 DOI: 10.3389/fpsyt.2024.1309501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/07/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction Emotional words are often used as stimulus material to explore the cognitive and emotional characteristics of individuals with depressive disorder, while normal individuals mostly rate the scores of affective words. Given that individuals with depressive disorder exhibit a negative cognitive bias, it is possible that their depressive state could influence the ratings of affective words. To enhance the validity of the stimulus material, we specifically recruited patients with depression to provide these ratings. Methods This study provided subjective ratings for 501 Chinese affective norms, incorporating 167 negative words selected from depressive disorder patients' Sino Weibo blogs, and 167 neutral words and 167 positive words selected from the Chinese Affective Word System. The norms are based on the assessments made by 91 patients with depressive disorder and 92 normal individuals, by using the paper-and-pencil quiz on a 9-point scale. Results Regardless of the group, the results show high reliability and validity. We identified group differences in three dimensions: valence, arousal, and self-relevance: the depression group rated negative words higher, but positive and neutral words lower than the normal control group. Conclusion The emotional perception affected the individual's perception of words, to some extent, this database expanded the ratings and provided a reference for exploring norms for individuals with different emotional states.
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Affiliation(s)
- Xinyue Xu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
- Department of Clinical Psychology, Dongguan Seventh People’s Hospital, Dongguan, China
| | - Fei An
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Shengjun Wu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Hui Wang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Qi Kang
- Center for Psychological Crisis Intervention, the 904th Hospital of the Joint Logistics Support Unit, Changzhou, China
| | - Ying Wang
- Department of Psychosomatic Medicine, Xi’an International Medical Center, Xi'an, China
| | - Ting Zhu
- Xinfeng Psychiatric Hospital, Xi ‘an Ninth Hospital, Xi'an, China
| | - Bing Zhang
- Department of Medical Psychology, the 984th Hospital of the Joint Logistics Support Unit, Beijing, China
| | - Wei Huang
- Department of Psychiatry, the 923th Hospital of the Joint Logistics Support Unit, Nanning, China
| | - Xufeng Liu
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
| | - Xiuchao Wang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, China
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Greco C, Raimo G, Amorese T, Cuciniello M, Mcconvey G, Cordasco G, Faundez-Zanuy M, Vinciarelli A, Callejas-Carrion Z, Esposito A. Discriminative Power of Handwriting and Drawing Features in Depression. Int J Neural Syst 2024; 34:2350069. [PMID: 38009869 DOI: 10.1142/s0129065723500697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.
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Affiliation(s)
- Claudia Greco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gennaro Raimo
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Terry Amorese
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marialucia Cuciniello
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gavin Mcconvey
- Action Mental Health, 27 Jubilee Rd, BT23 4YH, Newtownards, UK
| | - Gennaro Cordasco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marcos Faundez-Zanuy
- Tecnocampus Universitat Pompeu Fabra, Carrer d'Ernest Lluch 32 Mataro, Barcelona 08302, Spain
| | - Alessandro Vinciarelli
- University of Glasgow, School of Computing Science, 18 Lilybank Gardens Glasgow, G12,8RZ, Scotland
| | - Zoraida Callejas-Carrion
- Department of Languages and Computer Systems, Universidad de Granada, Periodista Daniel Saucedo Aranda Granada, 18071, Spain
| | - Anna Esposito
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
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Di Cara NH, Maggio V, Davis OSP, Haworth CMA. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. J Med Internet Res 2023; 25:e42734. [PMID: 37155236 DOI: 10.2196/42734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/23/2022] [Accepted: 03/15/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results. OBJECTIVE This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used. METHODS A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations. RESULTS The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention. CONCLUSIONS The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.
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Affiliation(s)
- Nina H Di Cara
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Valerio Maggio
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Oliver S P Davis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Claire M A Haworth
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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6
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Pool-Cen J, Carlos-Martínez H, Hernández-Chan G, Sánchez-Siordia O. Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation. Healthcare (Basel) 2023; 11:healthcare11071057. [PMID: 37046984 PMCID: PMC10094126 DOI: 10.3390/healthcare11071057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.
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Affiliation(s)
- Jorge Pool-Cen
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
| | - Hugo Carlos-Martínez
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- IxM CONACyT, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
| | - Gandhi Hernández-Chan
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- IxM CONACyT, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
| | - Oscar Sánchez-Siordia
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
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A systematic review of the available literature on the use of social media in brain tumor. GLOBAL KNOWLEDGE, MEMORY AND COMMUNICATION 2023. [DOI: 10.1108/gkmc-11-2022-0254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Purpose
The use of social media is one of the new technological options that has been recommended as a potential new strategy for delivering high-quality, high-value cancer prevention and management services. Despite the increasing use of social media, little research has been done on the use of social media in brain tumors. Therefore, this systematic review aims to provide a comprehensive review of the use of social media in brain tumor research.
Design/methodology/approach
A systematic search was performed in PubMed, Scopus and Web of Science from inception to August 1, 2022. English full-text articles evaluating social media use, benefit or content in brain tumor were considered.
Findings
Sixteen documents satisfied the inclusion criteria and were included in the final analysis. Most of the included studies (n = 11/16) were conducted and published by researchers in the USA. In terms of social media platform, most studies focused on Twitter (8/16, 50%) and YouTube (8/16, 50%), followed by Facebook (6/16, 37.5%) and Instagram (4/16, 25%). Most studies (n = 7/12) analyzed the content of brain tumor information provided on social media, followed by patients’ use of social media (n = 3/12) and the quality of information on social media (n = 3/12). The other three articles also examined patient recruitment, crowdfunding and caregiver use of social media.
Practical implications
By identifying the use, benefits and content of social media platforms in different settings, patients, clinicians and policymakers can better benefit from harnessing the power of social media in different ways, leading to improved health-care services.
Originality/value
To the authors knowledge, this is the first study to systematically examine social media use, benefits and content status in brain tumors.
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Krishnamurti T, Allen K, Hayani L, Rodriguez S, Rothenberger S, Moses-Kolko E, Simhan H. Using natural language from a smartphone pregnancy app to identify maternal depression. RESEARCH SQUARE 2023:rs.3.rs-2583296. [PMID: 36865248 PMCID: PMC9980211 DOI: 10.21203/rs.3.rs-2583296/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Depression is highly prevalent in pregnancy, yet it often goes undiagnosed and untreated. Language can be an indicator of psychological well-being. This longitudinal, observational cohort study of 1,274 pregnancies examined written language shared in a prenatal smartphone app. Natural language feature of text entered in the app (e.g. in a journaling feature) throughout the course of participants' pregnancies were used to model subsequent depression symptoms. Language features were predictive of incident depression symptoms in a 30-day window (AUROC = 0.72) and offer insights into topics most salient in the writing of individuals experiencing those symptoms. When natural language inputs were combined with self-reported current mood, a stronger predictive model was produced (AUROC = 0.84). Pregnancy apps are a promising way to illuminate experiences contributing to depression symptoms. Even sparse language and simple patient-reports collected directly from these tools may support earlier, more nuanced depression symptom identification.
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Venuturupalli S, Kumar A, Bunyan A, Davuluri N, Fortune N, Reuter K. Using Patient-Reported Health Data From Social Media to Identify Diverse Lupus Patients and Assess Their Symptom and Medication Expressions: A Feasibility Study. Arthritis Care Res (Hoboken) 2023; 75:365-372. [PMID: 35157364 PMCID: PMC9375779 DOI: 10.1002/acr.24868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 01/10/2022] [Accepted: 02/10/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications. METHODS We extracted public lupus-related Twitter messages (n = 47,715 tweets) in English posted by users (n = 8,446) in the US between September 1, 2017 and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using 2-tailed Z tests and a combination of 1-way analysis of variance tests and unpaired t-tests. RESULTS We found that lupus patients on Twitter are diverse in gender and race: approximately one-third (34.64%, 62 of 179) were persons of color (POCs), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Most of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709 of 8,446), flares (6.05%, 511 of 8,446), and fatigue (4.18%, 353 of 8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus. CONCLUSION Our results indicate that social media surveillance can provide valuable data of clinical relevance from the perspective of lupus patients. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups, such as POCs.
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Affiliation(s)
- Swamy Venuturupalli
- MD, Cedars-Sinai Medical Center, Los Angeles, CA, United States; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Amit Kumar
- BS, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Alden Bunyan
- BS, MHDS, Borra College of Health Sciences, Dominican University, IL, United States
| | - Nikhil Davuluri
- BS, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Natalie Fortune
- MS, RDN, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Katja Reuter
- PhD, Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, United States; Southern California Clinical and Translational Science Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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Klier K, Rommerskirchen T, Brixius K. #fitspiration: a comparison of the sport-related social media usage and its impact on body image in young adults. BMC Psychol 2022; 10:320. [PMID: 36575554 PMCID: PMC9793811 DOI: 10.1186/s40359-022-01027-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/19/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Following and posting sport-related content on social media is wide-spread among young people. To date, little is known about the interdependence between sport-related social media use and the thereby perceived personal body image. METHODS We conducted an online survey (N = 285) to examine how social media influences the sport-related body image. RESULTS In general, social media are frequently used for sport (n = 136, 47.7%). Resistance training correlated significantly with several motives of sport-related use of social media, and thus, represents the strong online presence of athletic sports. Less correlations could be found in team or other sports. Regarding the perception of body image, it was found that the group of rejecting (negative) body image significantly correlated with the emulation of social media mediated sport-related beauty and body ideals (r = 0.63, p = 0.001), as well as with increased body dissatisfaction when viewing sport-related posts on social media (r = 0.590, p = 0.001). Perceived social pressure and comparison were found to be mediators of the prevailing influence of social media usage. CONCLUSIONS These results reveal the importance of taking a closer look at socially shaped beauty and body ideals, especially in sport-related contents, striving for more educational campaigns such as Body Positivity and, above all, filtering information. Finally, future research is needed to gain deeper insight into young persons' usage behavior of social media and its impact on the individual's body image. Trial Registration The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of University of the Federal Armed Forces Munich, Germany (01/24/2022).
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Affiliation(s)
- Kristina Klier
- grid.7752.70000 0000 8801 1556Institut für Sportwissenschaft, Universität der Bundeswehr München, Neubiberg, Germany
| | - Tessa Rommerskirchen
- grid.7752.70000 0000 8801 1556Institut für Sportwissenschaft, Universität der Bundeswehr München, Neubiberg, Germany
| | - Klara Brixius
- grid.27593.3a0000 0001 2244 5164Institut für Kreislaufforschung und Sportmedizin, Deutsche Sporthochschule Köln, Cologne, Germany
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11
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Mayor E, Miché M, Lieb R. Associations between emotions expressed in internet news and subsequent emotional content on twitter. Heliyon 2022; 8:e12133. [PMID: 36561692 PMCID: PMC9763764 DOI: 10.1016/j.heliyon.2022.e12133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/27/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
We report on the first investigation of large-scale temporal associations between emotions expressed in online news media and those expressed on social media (Twitter). This issue has received little attention in previous research, although the study of emotions expressed on social media has bloomed owing to its importance in the study of mental health at the population level. Relying on automatically emotion-coded data from almost 1 million online news articles on disease and the coronavirus and more than 6 million tweets, we examined such associations. We found that prior changes in generic emotional categories (positive and negative emotions) in the news on the topic of disease were associated with lagged changes in these categories in tweets. Discrete negative emotions did not robustly feature this pattern. Emotional categories coded in online news stories on the coronavirus generally featured weaker and more disparate lagged associations with emotional categories coded in subsequent tweets.
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12
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Picazo-Sánchez L, Domínguez-Martín R, García-Marín D. Health Promotion on Instagram: Descriptive-Correlational Study and Predictive Factors of Influencers' Content. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15817. [PMID: 36497889 PMCID: PMC9739539 DOI: 10.3390/ijerph192315817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
The pandemic has accentuated the power that influencers have to influence their followers. Various scientific approaches highlight the lack of moral and ethical responsibility of these creators when disseminating content under highly sensitive tags such as health. This article presents a correlational statistical study of 443 Instagram accounts with more than one million followers belonging to health-related categories. This study aims to describe the content of these profiles and their authors and to determine whether they promote health as accounts that disseminate health-related content, identifying predictive factors of their content topics. In addition, it aims to portray their followers and establish correlations between the gender of the self-described health influencers, the characteristics of their audience and the messages that these prescribers share. Health promotion is not the predominant narrative among these influencers, who tend to promote beauty and normative bodies over health matters. A correlation is observed between posting health content, the gender of the influencers and the average age of their audiences. The study concludes with a discussion on the role of public media education and the improvement of ethical protocols on social networks to limit the impact of misleading and false content on sensitive topics, increasing the influence of real health prescribers.
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Affiliation(s)
- Laura Picazo-Sánchez
- Department of ICT Applied to Education and Media Literacy, Faculty of Education, Universidad Internacional de Valencia (VIU), 46002 Valencia, Spain
| | - Rosa Domínguez-Martín
- Department of Pedagogy, Faculty of Education, Universidad Internacional de Valencia (VIU), 46002 Valencia, Spain
| | - David García-Marín
- Department of Journalism and Corporate Communication, Faculty of Communication Sciences, Universidad Rey Juan Carlos (URJC), 28933 Móstoles, Spain
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13
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Arioz U, Smrke U, Plohl N, Mlakar I. Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models. Diagnostics (Basel) 2022; 12:2683. [PMID: 36359525 PMCID: PMC9689708 DOI: 10.3390/diagnostics12112683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 12/26/2023] Open
Abstract
Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
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Affiliation(s)
- Umut Arioz
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, The University of Maribor, 2000 Maribor, Slovenia
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
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Ulep AJ, Deshpande AK, Beukes EW, Placette A, Manchaiah V. Social Media Use in Hearing Loss, Tinnitus, and Vestibular Disorders: A Systematic Review. Am J Audiol 2022; 31:1019-1042. [DOI: 10.1044/2022_aja-21-00211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Background:
People are increasingly using social media outlets for gathering health-related information. There has also been considerable interest from researchers and clinicians in understanding how social media is used by the general public, patients, and health professionals to gather health-related information. Interest in the use of social media for audiovestibular disorders has also received attention, although published evidence synthesis of this use is lacking. The objective of this review article was to synthesize existing research studies related to social media use concerning hearing loss, tinnitus, and vestibular disorders.
Method:
Comprehensive searches were performed in multiple databases between October and November 2020 and again in June 2021 and March 2022, with additional reports identified from article citations and unpublished literature. This review article was presented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
Results:
A total of 1,512 articles were identified. Of these, 16 publications met the inclusion criteria. Overall, social media offered people the platform to learn about hearing loss, tinnitus, and vestibular disorders via advice and support seeking, personal experience sharing, general information sharing, and relationship building. Research studies were more common on information and user activities seen on Facebook Pages, Twitter, and YouTube videos. Misinformation was identified across all social media platforms for each of these conditions.
Conclusions:
Online discussions about audiovestibular disorders are evident, although inconsistencies in study procedures make it difficult to compare these discussion groups. Misinformation is a concern needing to be addressed during clinical consultations as well as via other public health means. Uniform guidelines are needed for research regarding the use of social media so that outcomes are comparable. Moreover, clinical studies examining how exposure to and engagement with social media information may impact outcomes (e.g., help seeking, rehabilitation uptake, rehabilitation use, and satisfaction) require exploration.
Supplemental Material:
https://doi.org/10.23641/asha.20667672
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Affiliation(s)
- Alyssa Jade Ulep
- Department of Speech and Hearing Sciences, Lamar University, Beaumont, TX
- Virtual Hearing Lab, University of Colorado School of Medicine and University of Pretoria, Aurora, CO
| | - Aniruddha K. Deshpande
- The Hear-Ring Lab, Department of Speech-Language-Hearing Sciences, Hofstra University,Hempstead, NY
| | - Eldré W. Beukes
- Virtual Hearing Lab, University of Colorado School of Medicine and University of Pretoria, Aurora, CO
- Vision and Hearing Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Aubry Placette
- Department of Speech and Hearing Sciences, Lamar University, Beaumont, TX
| | - Vinaya Manchaiah
- Virtual Hearing Lab, University of Colorado School of Medicine and University of Pretoria, Aurora, CO
- Department of Otolaryngology—Head & Neck Surgery, University of Colorado School of Medicine, Aurora
- UCHealth Hearing and Balance Clinic, University of Colorado Hospital, Aurora
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Gauteng, South Africa
- Department of Speech and Hearing, School of Allied Health Sciences, Manipal Academy of Higher Education, Karnataka, India
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15
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Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The Big Picture on Instagram Research: Insights from a Bibliometric Analysis. TELEMATICS AND INFORMATICS 2022. [DOI: 10.1016/j.tele.2022.101876] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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16
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Krohn H, Guintivano J, Frische R, Steed J, Rackers H, Meltzer-Brody S. App-Based Ecological Momentary Assessment to Enhance Clinical Care for Postpartum Depression: Pilot Acceptability Study. JMIR Form Res 2022; 6:e28081. [PMID: 35319483 PMCID: PMC8987954 DOI: 10.2196/28081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 01/30/2022] [Accepted: 02/09/2022] [Indexed: 01/23/2023] Open
Abstract
Background Wearable tracking devices and mobile health technology are increasingly used in an effort to enhance clinical care and the delivery of personalized medical treatment. Postpartum depression is the most frequently diagnosed complication of childbirth; however, significant gaps in screening and treatment remain. Objective This study aims to investigate the clinical utility, predictive ability, and acceptability of using ecological momentary assessment to collect daily mood, sleep, and activity data through the use of an Apple Watch and mobile app among women with postpartum depression. Methods This was a pilot study consisting of 3 in-person research visits over the course of a 6-week enrollment period. Questionnaires to assess depression, anxiety, and maternal functioning were periodically collected, along with daily self-reported symptoms and passively collected physiological data via an Apple Watch. Feedback was collected from study participants and the study clinician to determine the utility and acceptability of daily tracking. Logistic regression was used to determine whether mood scores in the 2 weeks before a visit predicted scores at follow-up. Compliance with daily assessments was also measured. Results Of the 26 women enrolled, 23 (88%) completed the 6-week study period. On average, the participants completed 67% (34.4/51.5 days) of all active daily assessments and 74% (38/51.5 days) of all passive measures. Furthermore, all 23 participants completed the 3 required visits with the research team. Predictive correlations were found between self-reported mood and Edinburgh Postnatal Depression Scale score at follow-up, self-reported anxiety and EDPS, and sleep quality and Edinburgh Postnatal Depression Scale. Conclusions Using ecological momentary assessment to track daily symptoms of postpartum depression using a wearable device was largely endorsed as acceptable and clinically useful by participants and the study clinician and could be an innovative solution to increase care access during the COVID-19 pandemic.
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Affiliation(s)
- Holly Krohn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jerry Guintivano
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rachel Frische
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jamie Steed
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Hannah Rackers
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Samantha Meltzer-Brody
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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17
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Connell NT, Weyand AC, Barnes GD. Use of Social Media in the Practice of Medicine. Am J Med 2022; 135:138-140. [PMID: 34560038 DOI: 10.1016/j.amjmed.2021.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Nathan T Connell
- Hematology Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass.
| | - Angela C Weyand
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor
| | - Geoffrey D Barnes
- Frankel Cardiovascular Center and Michigan Program on Value Enhancement, University of Michigan Health System, Ann Arbor
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18
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Social Media as It Interfaces with Psychosocial Development and Mental Illness in Transitional-Age Youth. Child Adolesc Psychiatr Clin N Am 2022; 31:11-30. [PMID: 34801149 DOI: 10.1016/j.chc.2021.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Social media (SM) can be defined as "a group of Internet-based applications that allow the creation and exchange of user-generated content." This includes formation of online communities and sharing of information, ideas, opinions, messages, images, and videos. Therefore, although all online video games would not necessarily count as SM, video games that allow for substantial sharing of information and development of online communities do fit this definition. SM has become an integral component of how people worldwide connect with friends and family, share personal content, and obtain news and entertainment. Use of SM is particularly prevalent among transitional-age youth, usually defined as individuals aged 16 to 24 years, who are at critical junctures around developmental tasks such as identity development and establishment of social norms.
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Smrke U, Mlakar I, Lin S, Musil B, Plohl N. Language, Speech, and Facial Expression Features for Artificial Intelligence-Based Detection of Cancer Survivors' Depression: Scoping Meta-Review. JMIR Ment Health 2021; 8:e30439. [PMID: 34874883 PMCID: PMC8691410 DOI: 10.2196/30439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/25/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. OBJECTIVE The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. METHODS We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. RESULTS The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. CONCLUSIONS This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence-supported screening for depression in cancer survivors.
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Affiliation(s)
- Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Simon Lin
- Science Department, Symptoma, Vienna, Austria.,Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Bojan Musil
- Department of Psychology, Faculty of Arts, University of Maribor, Maribor, Slovenia
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, University of Maribor, Maribor, Slovenia
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20
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Ptaszynski M, Zasko-Zielinska M, Marcinczuk M, Leliwa G, Fortuna M, Soliwoda K, Dziublewska I, Hubert O, Skrzek P, Piesiewicz J, Karbowska P, Dowgiallo M, Eronen J, Tempska P, Brochocki M, Godny M, Wroczynski M. Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media-A Pragmalinguistic Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11759. [PMID: 34831513 PMCID: PMC8624334 DOI: 10.3390/ijerph182211759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.
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Affiliation(s)
- Michal Ptaszynski
- Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Japan;
| | - Monika Zasko-Zielinska
- Department of Contemporary Polish Language, Faculty of Philology, University of Wrocław, 50-140 Wrocław, Poland;
| | - Michal Marcinczuk
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
- Department of Computational Intelligence, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Gniewosz Leliwa
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Marcin Fortuna
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
- Institute of English and American Studies, Glottodidactics and Natural Language Processing Division, University of Gdańsk, 80-308 Gdańsk, Poland
| | - Kamil Soliwoda
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Ida Dziublewska
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Olimpia Hubert
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Pawel Skrzek
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Jan Piesiewicz
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Paula Karbowska
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Maria Dowgiallo
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
- Institute of Clinical Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland
| | - Juuso Eronen
- Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Japan;
| | - Patrycja Tempska
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Maciej Brochocki
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Marek Godny
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
| | - Michal Wroczynski
- Samurai Labs, 81-824 Sopot, Poland; (M.M.); (G.L.); (M.F.); (K.S.); (I.D.); (O.H.); (P.S.); (J.P.); (P.K.); (M.D.); (P.T.); (M.B.); (M.G.); (M.W.)
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Laestadius LI, Craig KA, Campos-Castillo C. Perceptions of Alerts Issued by Social Media Platforms in Response to Self-injury Posts Among Latinx Adolescents: Qualitative Analysis. J Med Internet Res 2021; 23:e28931. [PMID: 34383683 PMCID: PMC8386397 DOI: 10.2196/28931] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/22/2021] [Accepted: 07/05/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND There is growing interest in using social media data to detect and address nonsuicidal self-injury (NSSI) among adolescents. Adolescents often do not seek clinical help for NSSI and may adopt strategies to obscure detection; therefore, social media platforms may be able to facilitate early detection and treatment by using machine learning models to screen posts for harmful content and subsequently alert adults. However, such efforts have raised privacy and ethical concerns among health researchers. Little is currently known about how adolescents perceive these efforts. OBJECTIVE The aim of this study is to examine perceptions of automated alerts for NSSI posts on social media among Latinx adolescents, who are at risk for NSSI yet are underrepresented in both NSSI and health informatics research. In addition, we considered their perspectives on preferred recipients of automated alerts. METHODS We conducted semistructured, qualitative interviews with 42 Latinx adolescents between the ages of 13 and 17 years who were recruited from a nonprofit organization serving the Latinx community in Milwaukee, Wisconsin. The Latinx population in Milwaukee is largely of Mexican descent. All interviews were conducted between June and July 2019. Transcripts were analyzed using framework analysis to discern their perceptions of automated alerts sent by social media platforms and potential alert recipients. RESULTS Participants felt that automated alerts would make adolescents safer and expedite aid before the situation escalated. However, some worried that hyperbolic statements would generate false alerts and instigate conflicts. Interviews revealed strong opinions about ideal alert recipients. Parents were most commonly endorsed, but support was conditional on perceptions that the parent would respond appropriately. Emergency services were judged as safer but inappropriate for situations considered lower risk. Alerts sent to school staff generated the strongest privacy concerns. Altogether, the preferred alert recipients varied by individual adolescents and perceived risks in the situation. None raised ethical concerns about the collection, analysis, or storage of personal information regarding their mental health status. CONCLUSIONS Overall, Latinx adolescents expressed broad support for automated alerts for NSSI on social media, which indicates opportunities to address NSSI. However, these efforts should be co-constructed with adolescents to ensure that preferences and needs are met, as well as embedded within broader approaches for addressing structural and cultural barriers to care.
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Affiliation(s)
- Linnea I Laestadius
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Katherine A Craig
- Department of Sociology, University of Colorado Boulder, Boulder, CO, United States
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