4,977 research outputs found

    What Twitter Profile and Posted Images Reveal About Depression and Anxiety

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    Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

    Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey

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    Online social media provides a channel for monitoring people\u27s social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns

    Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media

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    Social media offers a unique lens to observe users emotions and subjective feelings toward critical events or topics and has been widely used to investigate public sentiment during crises, e.g., the COVID-19 pandemic. However, social media use varies across demographic groups, with younger people being more inclined to use social media than the older population. This digital divide could lead to biases in data representativeness and analysis results, causing a persistent challenge in research based on social media data. This study aims to tackle this challenge through a case study of estimating the public sentiment about the COVID-19 using social media data. We analyzed the pandemic-related Twitter data in the United States from January 2020 to December 2021. The objectives are: (1) to elucidate the uneven social media usage among various demographic groups and the disparities of their emotions toward COVID-19, (2) to construct an unbiased measurement for public sentiment based on social media data, the Sentiment Adjusted by Demographics (SAD) index, through the post-stratification method, and (3) to evaluate the spatially and temporally evolved public sentiment toward COVID-19 using the SAD index. The results show significant discrepancies among demographic groups in their COVID-19-related emotions. Female and under or equal to 18 years old Twitter users expressed long-term negative sentiment toward COVID-19. The proposed SAD index in this study corrected the underestimation of negative sentiment in 31 states, especially in Vermont. According to the SAD index, Twitter users in Wyoming (Vermont) posted the largest (smallest) percentage of negative tweets toward the pandemic

    Self-Compassion, Body Image Dissatisfaction, and Negative Social Comparisons in Adolescents Utilizing Social Networking Sites

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    This quantitative survey study implemented a cross-sectional, correlational design. The present study explored the relationship between self-compassion, body image, and negative social comparisons in a sample consisting of adolescents who use social networking sites. Despite noteworthy limitations, this study elucidates the benefits associated with higher levels of self-compassion in adolescence. In line with previous studies, adolescents reported frequent use of social networking sites, primarily facilitated by smartphones. Although the constant accessibility of social networking sites via smartphones has been associated with negative outcomes, an important finding in this study was the lack of significant relationship between overall time spent on social networking sites, lower levels of self-compassion, negative social comparisons, and negative body image. Nevertheless, a significant relationship was found between negative body image and belonging to more than three social networking sites. These findings highlight the necessity of future research studies which investigate the differential impact of various social networking sites, how certain online behaviors may predispose adolescents to diminished overall psychological well-being, and the influence of preexisting psychopathology. Lastly, preventative measures, such as treatment programs that enhance self-compassion and media literacy campaigns, are suggested to buffer adolescents against the negative consequences associated with maladaptive social networking site

    A Scorecard Method for Detecting Depression in Social Media Users

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    A Scorecard Method for Detecting Depression in Social Media Users Netsanet Tefera Lina Zhou University of Maryland, Baltimore County {netsa2, zhoul}@umbc.edu Abstract Depression is one of the most prevalent mental health disorders today. Depression has become the leading causes of disability and premature mortality partly due to a lack of effective methods for early detection. This research explores how social media can be used as a tool to detect the level of depression in its users by proposing a scorecard method based on their user profiles. In the proposed method, depression is measured by a selected set of key dimensions along with their specific indicators, which are weighted based on their importance for signaling depression in the literature. To evaluate the scorecard method, we compared three types of social media users: users who committed suicide due to depression, users who were likely suffering from depression, and users who were unlikely suffering from depression. The empirical results demonstrate the effectiveness of the scorecard method in detecting depression

    The Undisclosed Dangers of Parental Sharing on Social Media: A Content Analysis of Sharenting Images on Instagram

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    Sharenting is a new term used to define the action of parents posting about their children online. Social media provides parents with an easy to use outlet for image distribution to all family and friends that simultaneously archives the images into a digital baby book. While convenient, once publicly posted anyone can gain access to the images of the children. Instagram is a favorable social media channel for sharenting. A popular hashtag on Instagram, #letthembelittle, contains 8 million posts dedicated to child imagery. A set of 300 randomly selected images under the hashtag were coded. Images tended to contain personal information such as the child’s name, age, and location. Communication Privacy Management and Uses and Gratifications theories provided the theoretical frameworks for this study. The results suggested a possibly dangerous pattern of parental oversharing that could negatively impact the child and the child’s safety

    Spartan Daily, October 24, 2018

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    Volume 151, Issue 28https://scholarworks.sjsu.edu/spartan_daily_2018/1070/thumbnail.jp
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