A corpus-based stylistic analysis of online suicide notes retrieved from Reddit

Jaafar, Eman Adil; Jasim, Haya Abdul-Salam

doi:10.1080/23311983.2022.2047434

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…There are several types of features pertinent to language and vocal data. Python libraries such librosa and NLTK can be used to extract various linguistic features, including lexical (e.g., total number of words, Brunet’s Index, and Honor’s Statistic ( 59 )), syntactic, semantic, and pragmatic features ( 60 ).…”

Section: A Standardized Methods To Develop Vocal and Textual Systems ...mentioning

confidence: 99%

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

Parsapoor¹,

Koudys²,

Ruocco³

2023

Front. Psychiatry

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Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.

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Section: A Standardized Methods To Develop Vocal and Textual Systems ...mentioning

confidence: 99%

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

Parsapoor¹,

Koudys²,

Ruocco³

2023

Front. Psychiatry

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“…Consequently, language is a powerful indicator of personality, social and emotional status and mental health (Pennebaker et al , 2003) which can be found in several previous studies. For instance, Coppersmith et al (2015) studied a broad range of mental health conditions in Twitter data by applying word categories software, Gkotsis et al (2016) investigated the language of Reddit posts specific to mental health using word-based classification, and Adil Jaafar and Abdul-Salam Jasim (2022) examined online suicide text on Reddit using a corpus-based analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated entitymetrics analysis for health information on bipolar disorder using social media data and scientific literature

Timakum

Song

Kim

2022

AJIM

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PurposeThis study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.Design/methodology/approachReddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.FindingsMental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.Originality/valueMental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

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“…From a cognitive pragmatic standpoint, it appears that the participants in the scientific discourse on carcinogen risk, in particular, adjust their category choice to match the mental states that are typical in this situation for both actors and participants. The common interest shared by both actors and partners is that they are searching for information that they are deliberately producing and receiving (Jaafar & Jasim, 2022;Khalil, 2022). Carcinogens represent a vital issue that has been continuously discussed by scientific discourse.…”

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confidence: 99%

Constructing Imminent Carcinogenic Attack in English and Arabic Scientific Discourse: A Corpus-Based Contrastive Study

Khalil¹,

Al-Zubaidi²

2022

JCEW

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Cancer is one of the critical health concerns. Health authorities around the world have devoted great attention to cancer and cancer causing factors to achieve control against the increasing rate of cancer. Carcinogens are the most salient factors that are accused of causing a considerable rate of cancer cases. Scientists, in different fields of knowledge, keep warning people of the imminent attack of carcinogens which are surrounding people in the environment and may launch their attack at any moment. The present paper aims to investigate the linguistic construction of the imminent carcinogen attack in English and Arabic scientific discourse. Such an investigation contributes to enhancing the scientists’ awareness of the linguistic conduct they follow in attracting people’s attention towards the risk of the approaching attack. The linguistic awareness also helps maintain better promotion of people’s pre-emptive responses that can reduce the potential for cancer cases. To achieve this aim, the paper adopts dual methodological procedures of qualitative and quantitative analyses. Cap’s (2013) proximization theory of threat and crisis construction is adopted for both qualitative and quantitative procedures. The mathematical calculations and statistical results for discourses in both languages are maintained by corpus linguistic analysis using Anthony’s (2019) software, AntConc. The paper has come up to certain conclusions that shed light on the similarities and differences in the construction of the imminent carcinogen attack in both languages. English scientific discourse has shown more reliance on temporal proximization to envisage the imminent attack of carcinogens against human bodies. Categories 1, 2, 3 and 5 are all more dominant in the English scientific discourse. However, both English and Arabic discourses show diversity in the density and employment of the lexico- grammatical tools (categories) that manifest the carcinogen attack.

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A corpus-based stylistic analysis of online suicide notes retrieved from Reddit

Cited by 8 publications

References 10 publications

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk

Integrated entitymetrics analysis for health information on bipolar disorder using social media data and scientific literature

Constructing Imminent Carcinogenic Attack in English and Arabic Scientific Discourse: A Corpus-Based Contrastive Study

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