BackgroundSuicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text.MethodsFirst, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text.Results & conclusionsTo our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.

A valid mechanism for suicide detection and intervention to a wider population online has not yet been fully established. With the increasing suicide rate, we proposed an approach that aims to examine temporal patterns of potential suicidal ideations and behaviors on Twitter to better understand their risk factors and time-varying features. It identifies latent suicide topics and then models the suicidal topic–related score time series to quantitatively represent behavior patterns on Twitter. After evaluated on a collection of suicide-related tweets in 2016, 13 key risk factors were discovered and the temporal patterns of suicide behavior on different days during 1 week were identified to highlight the distinct time-varying features related to different risk factors. This study is practical to help public health services and others to develop refined prevention strategies, to monitor and support a population of high-risk at right moments.

Purpose An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media. Design/methodology/approach This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users. Findings The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5. Practical implications The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers. Originality/value This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.

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