Discovery of Informal Topics from Post Traumatic Stress Disorder Forums

Grant, Reilly N.; Kucher, David; Leon, Ana M.; Gemmell, Jonathan; Raicu, Daniela

doi:10.1109/icdmw.2017.65

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…The anonymity of Reddit also allows its users to talk more freely about their problems compared to Facebook users (Choudhury & De, 2014). Thorstad and Wolff (2019) showed that posts in mental disorder subreddits are almost exclusively about the individuals' subjective experiences with the disorders, and other studies have found that the topics of discussion in Reddit mental disorder support groups reflect clinically relevant content (e.g., Chakravorti, Law, Gemmell, & Raicu, 2018;Grant, Kucher, Leon, Gemmell, & Raicu, 2017;Thorstad & Wolff, 2019).…”

Section: Diagnosis and User-generated Contentmentioning

confidence: 99%

Differential Diagnosis of Borderline Personality Disorder: Machine Learning Models of Subjective Experiences

Horvath¹,

Dras²,

Forbes³

2023

Preprint

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The present study aimed to capture the subjective experiences that distinguish BPD from closely related mental disorders to aid differential diagnosis. Posts from Reddit were downloaded from seven mental health discussion groups. The topics discussed in each post were extracted using a combination of machine learning approaches. Logistic regression models were used to classify whether posts originated from the BPD support group versus other support groups. The average classification performance was well above chance, even relying on only 25 subjective experiences, and after excluding topics that were objective markers of diagnoses (e.g., names of the disorders or medications). Fear of abandonment emerged as the key differentiator of BPD, and 11 of the 25 topics related directly to the definition of BPD in the DSM-5. However, several of these were typical of other disorders as well, raising questions about their diagnostic utility.

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Section: Diagnosis and User-generated Contentmentioning

confidence: 99%

Differential Diagnosis of Borderline Personality Disorder: Machine Learning Models of Subjective Experiences

Horvath¹,

Dras²,

Forbes³

2023

Preprint

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“…Because of the relative anonymity allowed by certain social media platforms, large social media datasets are useful for researchers studying topics in health informatics including sensitive medical issues, atypical behaviors, and interpersonal conflict. Pushshift data has been used by researchers studying eating disorders and weight loss [41], addiction and substance abuse [8,9,14,19,92,128], sexually transmitted infections [87], difficult child-rearing problems [5], and various mental health challenges [23,36,48,63,62,106,109].…”

Section: Author Flair Textmentioning

confidence: 99%

The Pushshift Reddit Dataset

Baumgartner¹,

Zannettou²,

Keegan³

et al. 2020

Preprint

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Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves.Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically.In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

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“…Although Reddit has been used as a data source for online social media analysis, relatively little research has been conducted on event detection in Reddit. Instead, the research has more often focused on community analysis [15,16], topic discovery [13,37] or on summarizing posts and comments [22]. Most analyses have focused on event summarization as opposed to event detection.…”

Section: Event Detection In Social Mediamentioning

confidence: 99%

“…Social media provides a rich, observable data source of dynamic social networks, and it has been extensively studied to model both online and offline human social behavior. Data generated by online social networks have been used to detect significant events in real-time [7,31], track the spread and sentiment of information [4,29,32], monitor population health and safety [13,33,34], etc. Reddit (www.reddit.com) is one such social media platform, generating large volumes of content shared by self-organizing, topically-oriented communities.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

Shah

Hurley

Liu³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases. 1 This distinction is subtle. In the context of this work we assume a 1:1 mapping between the (observable) network and the corresponding (sub)community it represents, and use these terms interchangeably.

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Discovery of Informal Topics from Post Traumatic Stress Disorder Forums

Cited by 6 publications

References 32 publications

Differential Diagnosis of Borderline Personality Disorder: Machine Learning Models of Subjective Experiences

Differential Diagnosis of Borderline Personality Disorder: Machine Learning Models of Subjective Experiences

The Pushshift Reddit Dataset

Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

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