A Study of Users with Suicidal Ideation on Sina Weibo

Wang, Zheng; Yu, Guang; Tian, Xianyun; Tang, Jingyun; Yan, Xiangbin

doi:10.1089/tmj.2017.0189

Cited by 17 publications

(10 citation statements)

References 32 publications

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“…In this study, we identified 3039 PSI in a Chinese suicidal community on Weibo and explored their behavior in the community. The finding that the agglomeration behavior of these PSI compared with the findings from previous study (114 PSI detected from 1 million Weibo users [37]), highlights the importance of suicide prevention at community level. To the knowledge of the researchers, this is the first study to report the behavior of PSI at community level.…”

Section: Discussionmentioning

confidence: 51%

“…As PSI are very likely to tell others about their suicidal thoughts on Weibo, researchers are trying to detect and help PSI according to their postings on Weibo [36]. For example, by studying the postings PSI published on Weibo, Wang et al [37] were able to conclude that PSI are more active at night, and more women than men report their suicidal ideation. Some researchers explored the linguistic features of PSI on Weibo, and found that PSI may use less verbs, and post more words in their updates [38].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

Wang

Tian

2018

IJERPH

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People with suicidal ideation (PSI) are increasingly using social media to express suicidal feelings. Researchers have found that their internet-based communication may lead to the spread of suicidal ideation, which presents a set of challenges for suicide prevention. To develop effective prevention and intervention strategies that can be efficiently applied in online communities, we need to understand the behavior of PSI in internet-based communities. However, to date there have been no studies that specifically focus on the behavior of PSI in Chinese online communities. A total of 4489 postings in which users explicitly expressed their suicidal ideation were labeled from 560,000 postings in an internet-based suicidal community on Weibo (one of the biggest social media platforms in China) to explore their behavior. The results reveal that PSI are significantly more active than other users in the community. With the use of social network analysis, we also found that the more frequently users communicate with PSI, the more likely that users would become suicidal. In addition, Chinese women may be more likely to be at risk of suicide than men in the community. This study enriches our knowledge of PSI’s behavior in online communities, which may contribute to detecting and assisting PSI on social media.

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Section: Discussionmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

Wang

Tian

2018

IJERPH

Self Cite

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“…One study has found that public concern and engagement in protective behaviours increased when the threat of the H1N1 outbreak increased and decreasing when the perceived risk declined 29 . Second, content analysis of social networks posts will enable researchers to analyse human attitudes or reactions towards specific health hazards 30 . The data from social media may help identify individuals with anxiety or fears about infectious diseases who are not identified by the traditional clinical or survey approaches.…”

Section: Discussionmentioning

confidence: 99%

Avian Influenza A (H7N9) and related Internet search query data in China

Chen

Zhang

et al. 2019

Sci Rep

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The use of Internet-based systems for infectious disease surveillance has been increasingly explored in recent years. However, few studies have used Internet search query or social media data to monitor spatial and temporal trends of avian influenza in China. This study investigated the potential of using search query and social media data in detecting and monitoring avian influenza A (H7N9) cases in humans in China. We collected weekly data on laboratory-confirmed H7N9 cases in humans, as well as H7N9-related Baidu Search Index (BSI) and Weibo Posting Index (WPI) data in China from 2013 to 2017, to explore the spatial and temporal trends of H7N9 cases and H7N9-related Internet search queries. Our findings showed a positive relationship of H7N9 cases with BSI and WPI search queries spatially and temporally. The outbreak threshold time and peak time of H7N9-related BSI and WPI searches preceded H7N9 cases in most years. Seasonal autoregressive integrated moving average (SARIMA) models with BSI ( β = 0.008, p < 0.001) and WPI ( β = 0.002, p = 0.036) were used to predict the number of H7N9 cases. Regression tree model analysis showed that the average H7N9 cases increased by over 2.4-fold (26.8/11) when BSI for H7N9 was > = 11524. Both BSI and WPI data could be used as indicators to develop an early warning system for H7N9 outbreaks in the future.

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“…The health care system was further overburdened as patients with mild symptoms sought hospitalization instead of self-isolation, mainly due to the anxiety and panic instigated by the epidemic [7]. After failing to be admitted to a hospital, patients sought help on Sina Weibo, a Chinese microblogging site similar to Twitter that allows people to communicate and share information instantly [8]. Social media has become an important channel for promoting risk communication during the crisis [9,10] and can be used to measure public attention given to public health emergencies [11], such as H7N9 [12][13][14], Ebola [9,[15][16][17][18][19], Zika virus [10,20,21], Middle East respiratory syndrome (MERS-CoV) [22], and Dengue fever [23].…”

Section: Introductionmentioning

confidence: 99%

Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

Huang¹,

Xu²,

Cai³

et al. 2020

J Med Internet Res

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Background In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. Objective This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. Methods We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients’ home and the nearest designated hospital was calculated using the geographic information system ArcGIS. Results All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital. Conclusions Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan’s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.

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A Study of Users with Suicidal Ideation on Sina Weibo

Abstract: Our findings may help to identify individuals with suicidal ideation who are not identified by the traditional clinical approach. Consequently, detecting and helping individuals who may be at risk of committing suicide may become more efficient.

Cited by 17 publications

References 32 publications

Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

Avian Influenza A (H7N9) and related Internet search query data in China

Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

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