Digital Wellbeing Redefined: Toward User-Centric Approach for Positive Social Media Engagement
- URL: http://arxiv.org/abs/2403.05723v2
- Date: Tue, 19 Mar 2024 00:30:12 GMT
- Title: Digital Wellbeing Redefined: Toward User-Centric Approach for Positive Social Media Engagement
- Authors: Yixue Zhao, Tianyi Li, Michael Sobolev,
- Abstract summary: This paper introduces a new perspective centered around empowering positive social media experiences.
We present PauseNow, an innovative digital wellbeing intervention designed to align users' digital behaviors with their intentions.
- Score: 4.37909887741567
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The prevalence of social media and its escalating impact on mental health has highlighted the need for effective digital wellbeing strategies. Current digital wellbeing interventions have primarily focused on reducing screen time and social media use, often neglecting the potential benefits of these platforms. This paper introduces a new perspective centered around empowering positive social media experiences, instead of limiting users with restrictive rules. In line with this perspective, we lay out the key requirements that should be considered in future work, aiming to spark a dialogue in this emerging area. We further present our initial effort to address these requirements with PauseNow, an innovative digital wellbeing intervention designed to align users' digital behaviors with their intentions. PauseNow leverages digital nudging and intention-aware recommendations to gently guide users back to their original intentions when they "get lost" during their digital usage, promoting a more mindful use of social media.
Related papers
- The Psychological Impacts of Algorithmic and AI-Driven Social Media on Teenagers: A Call to Action [0.0]
This study investigates the meta-issues surrounding social media.
Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media contribute to a profound psychological impact on individuals.
This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the constant bombardment of notifications and updates that characterize their social media experience.
arXiv Detail & Related papers (2024-08-19T18:49:12Z) - Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation [0.39321523855648755]
We propose an approach to enhance Interpersonal Emotion Regulation on online platforms through content recommendation.
The proposed recommendation system is expected to blend system-initiated and user-initiated emotion regulation.
The study collects 37.5K instances of user posts and interactions on Reddit over a year to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system.
arXiv Detail & Related papers (2024-08-05T10:27:28Z) - Advancing a Consent-Forward Paradigm for Digital Mental Health Data [39.14432077937818]
Service users are given little say over how their data is collected, shared, or used to generate revenue for private companies.
We propose an alternative approach that is attentive to this history: the consent-forward paradigm.
This paradigm embeds principles of affirmative consent in the design of digital mental health tools and services.
arXiv Detail & Related papers (2024-04-22T19:39:35Z) - Social Reward: Evaluating and Enhancing Generative AI through
Million-User Feedback from an Online Creative Community [63.949893724058846]
Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content.
This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework.
We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform.
arXiv Detail & Related papers (2024-02-15T10:56:31Z) - Who can help me? Reconstructing users' psychological journeys in
depression-related social media interactions [0.13194391758295113]
We investigate several popular mental health-related Reddit boards about depression.
We reconstruct users' psychological/linguistic profiles together with their social interactions.
Our approach opens the way to data-informed understandings of psychological coping with mental health issues through social media.
arXiv Detail & Related papers (2023-11-29T14:45:11Z) - Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels.
We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
arXiv Detail & Related papers (2022-11-14T18:47:26Z) - Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system.
Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
arXiv Detail & Related papers (2022-07-25T14:37:18Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state.
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
arXiv Detail & Related papers (2020-09-21T03:45:14Z) - Predicting User Emotional Tone in Mental Disorder Online Communities [2.365702128814616]
We analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users.
Using the emotional tone of users' writing as a proxy for emotional state, we uncover relationships between user interactions and state changes.
We build models based on SOTA text embedding techniques and RNNs to predict shifts in emotional tone.
arXiv Detail & Related papers (2020-05-15T11:25:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.