Covariance in diurnal patterns of suicide-related expressions on Twitter and recorded suicide deaths

Fahey, Robert A.; Boo, Jeremy; Ueda, Michiko

doi:10.1016/j.socscimed.2020.112960

Cited by 20 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As suicide prevention remains a pressing and complex issue, a large portion of the research throughput in the field focuses on detecting patterns that could be related to it [48], [69], [72], [74], [78], [79], [125], [193]. Using posts from suicide survivors published on Reddit, a method for identifying and classifying mentions of suicide attempts and their underlying methods was proposed [48].…”

Section: A Applications Related To Mental Healthmentioning

confidence: 99%

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

et al. 2022

View full text Add to dashboard Cite

Section: A Applications Related To Mental Healthmentioning

confidence: 99%

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

et al. 2022

View full text Add to dashboard Cite

“…Authors have examined the mental state of social media users in many languages.The majority of papers in the field are written in English [11,8,12,13,14,15,16,17,18,19,20,21,22,23]. The Chinese language was the second most used language in the published studies [24,25,26,27]. Further studies were completed in Spanish [28,29], and Russian [30], and even Japanese [21] and Filipino or Taglish [1].The distribution of articles over the platforms can be observed in Figure 2.…”

Section: Languages Of Textual Datamentioning

confidence: 99%

“…This trend showed a positive correlation among suicide deaths for people aged 15 to 44 years but negative among adults over 45 years old. Nighttime tweets showed a significant relationship between self-disgust words and words that indicate direct suicidal ideation [27].…”

Section: Temporal Behavior Problemmentioning

confidence: 99%

Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods

Asma¹,

Alhothali²

2022

Preprint

View full text Add to dashboard Cite

Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their personal stories and struggles. Negative feelings that express hardship, thoughts of death, and self-harm are widespread in social media, especially among young generations. Therefore, using social media to detect and identify suicidal ideation will help provide proper intervention that will eventually dissuade others from self-harming and committing suicide and prevent the spread of suicidal ideations on social media. Many studies have been carried out to identify suicidal ideation and behaviors in social media. This paper presents a comprehensive summary of current research efforts to detect suicidal ideation using machine learning algorithms on social media. This review 24 studies investigating the feasibility of social media usage for suicidal ideation detection is intended to facilitate further research in the field and will be a beneficial resource for researchers engaged in suicidal text classification.

show abstract

“…For example, Eichstaedt et al (2018) established that Facebook status updates predict medical-record-documented depression up to three months before diagnosis with fair accuracy (Eichstaedt et al, 2018). Online sources such as Twitter (Fahey, Boo, & Ueda, 2020; Roy et al, 2020) and Reddit (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) have been leveraged for content and shifts in writing style, word choice, and other natural language indicators that could serve as sentinel signals of an emerging suicidal crisis, and could be used in epidemiological surveillance moving forward as the field and methods become more sophisticated. However, concerns about intrusion and privacy require careful ethical assessment as this field moves forward.…”

Section: Novel Sources Of Surveillance Hold Promise To Augment Existimentioning

confidence: 99%

Suicide and the agent–host–environment triad: leveraging surveillance sources to inform prevention

et al. 2021

View full text Add to dashboard Cite

Suicide in the US has increased in the last decade, across virtually every age and demographic group. Parallel increases have occurred in non-fatal self-harm as well. Research on suicide across the world has consistently demonstrated that suicide shares many properties with a communicable disease, including person-to-person transmission and point-source outbreaks. This essay illustrates the communicable nature of suicide through analogy to basic infectious disease principles, including evidence for transmission and vulnerability through the agent–host–environment triad. We describe how mathematical modeling, a suite of epidemiological methods, which the COVID-19 pandemic has brought into renewed focus, can and should be applied to suicide in order to understand the dynamics of transmission and to forecast emerging risk areas. We describe how new and innovative sources of data, including social media and search engine data, can be used to augment traditional suicide surveillance, as well as the opportunities and challenges for modeling suicide as a communicable disease process in an effort to guide clinical and public health suicide prevention efforts.

show abstract

Covariance in diurnal patterns of suicide-related expressions on Twitter and recorded suicide deaths

Cited by 20 publications

References 23 publications

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods

Suicide and the agent–host–environment triad: leveraging surveillance sources to inform prevention

Contact Info

Product

Resources

About