Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information spread across social media platforms holds valuable indicators to effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

BackgroundSocial media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer.ObjectiveOur aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]).MethodsFirst, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life.ResultsAmong the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus.ConclusionsWe found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.

Abstract. Automatically detect suicidal people in social networks is a real social issue. In France, suicide attempt is an economic burden with strong socio-economic consequences. In this paper, we describe a complete process to automatically collect suspect tweets according to a vocabulary of topics suicidal persons are used to talk. We automatically capture tweets indicating suicidal risky behaviour based on simple classification methods. An interface for psychiatrists has been implemented to enable them to consult suspect tweets and profiles associated with these tweets. The method has been validated on real datasets. The early feedback of psychiatrists is encouraging and allow to consider a personalised response according to the estimated level of risk.

BackgroundCervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz.ObjectiveThe objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign.MethodsWe conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users’ status. Verbatims were thematically and then statistically analyzed.ResultsA total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P<.001), female gender (OR 3.752, 95% CI 2.133-6.598, P<.001), and people who live in the United Kingdom (OR 2.097, 95% CI 1.447-3.038, P<.001). Nonsensitizing tweets were statistically more posted by a nonhealth or nonmedia company (OR 0.558, 95% CI 0.383-0.814, P<.001).ConclusionsThis study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated.

BackgroundPatients and health care professionals are becoming increasingly preoccupied in complementary and alternative medicine (CAM) that can also be called nonpharmacological interventions (NPIs). In just a few years, this supportive care has gone from solutions aimed at improving the quality of life to solutions intended to reduce symptoms, supplement oncological treatments, and prevent recurrences. Digital social networks are a major vector for disseminating these practices that are not always disclosed to doctors by patients. An exploration of the content of exchanges on social networks by patients suffering from breast cancer can help to better identify the extent and diversity of these practices.ObjectiveThis study aimed to explore the interest of patients with breast cancer in CAM from posts published in health forums and French-language social media groups.MethodsThe retrospective study was based on a French database of 2 forums and 4 Facebook groups between June 3, 2006, and November 17, 2015. The extracted, anonymized, and compiled data (264,249 posts) were analyzed according to the occurrences associated with the NPI categories and NPI subcategories, their synonyms, and their related terms.ResultsThe results showed that patients with breast cancer use mainly physical (37.6%) and nutritional (31.3%) interventions. Herbal medicine is a subcategory that was cited frequently. However, the patients did not mention digital interventions.ConclusionsThis exploratory study of the main French forums and discussion groups indicates a significant interest in CAM during and after treatments for breast cancer, with primarily physical and nutritional interventions complementing approved treatments. This study highlights the importance of accurate information (vs fake medicine), prescription and monitoring of these interventions, and the mediating role that health professionals must play in this regard.

a b s t r a c tToday, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly. (J. Rabatel), bringay@lirmm.fr (S. Bringay), poncelet@lirmm.fr (P. Poncelet). 1

Attention about the risks of online social networks (SNs) has been called upon reports describing their use to express emotional distress and suicidal ideation or plans. On the Internet, cyberbullying, suicide pacts, Internet addiction, and “extreme” communities seem to increase suicidal behavior (SB). In this study, the scientific literature about SBs and SNs was narratively reviewed. Some authors focus on detecting at‐risk populations through data mining, identification of risks factors, and web activity patterns. Others describe prevention practices on the Internet, such as websites, screening, and applications. Targeted interventions through SNs are also contemplated when suicidal ideation is present. Multiple predictive models should be defined, implemented, tested, and combined in order to deal with the risk of SB through an effective decision support system. This endeavor might require a reorganization of care for SNs users presenting suicidal ideation.

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