11,029 research outputs found
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Self-reported diagnosis statements have been widely employed in studying
language related to mental health in social media. However, existing research
has largely ignored the temporality of mental health diagnoses. In this work,
we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported
depression diagnosis posts from Reddit that include temporal information about
the diagnosis. Annotations include whether a mental health condition is present
and how recently the diagnosis happened. Furthermore, we include exact temporal
spans that relate to the date of diagnosis. This information is valuable for
various computational methods to examine mental health through social media
because one's mental health state is not static. We also test several baseline
classification and extraction approaches, which suggest that extracting
temporal information from self-reported diagnosis statements is challenging.Comment: 6 pages, accepted for publication at the CLPsych workshop at
NAACL-HLT 201
Deconstructing the Model Minority Myth: Exploring Health Risk Behaviors of American Asian and Pacific Islander Young Adults
The model minority stereotype describes Asian and Pacific Islanders (API) as the epitome of assimilation into U.S. culture using hard work, intelligence, high educational attainment, and economic success to overcome the challenges of discrimination and recent immigration. Adopted model minority pressures assume a life of their own, with origins in childhood that are amplified during adolescence and young adulthood. In response to evidence of increased vulnerability to HIV and other sexually transmitted infection exposure, the present study compared prevalence estimates of health risk behaviors of API and cross-ethnic college students (N = 1,880). Self-reported alcohol use and abuse tendencies, legal and illicit drug use, abuse and misuse, as well as HIV- and other STI-related sexual risk were compared. Results of independent samples t-tests revealed that API displayed greater risk for alcohol use, abuse, dependence, and negative outcomes related to use. After controlling for differences in the 90-day prevalence of sexual activity, Cochran-Mantel-Haenszel and chi-square analyses indicated significantly greater behavioral risks for infection among API. API males were nearly twice as likely as their cross-ethnic peers to engage in insertive oral and anal sex without a condom to the point of ejaculation. While reporting fewer risks compared to their male counterparts, API females were significantly more likely than their cross-ethnic peers to engage in behaviors which may enhance exposure to infection. Such findings suggest a shrinking cultural divide with regard to risk behaviors on college campuses, as well as a lack of perceived HIV and other sexually transmitted infection risk among API students. As universities continue to foster cultures of diversity, the unique experiences and prevention-based needs of API students must be addressed
Depression and Self-Harm Risk Assessment in Online Forums
Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
("RSDD") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4,
FastText baseline, and CNN-
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
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