The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study

Emerging trends in domestic homicide/femicide in Greece over the period 2010-2021

Temporal trends in epidemiological parameters of domestic homicide and femicide in Greece over the last decade have not yet been studied. We conducted this study to fulfill this purpose. Specifically, we conducted a retrospective epidemiological study using 11-year data from the official nationwide Hellenic Police Archives and statistically analyzed data regarding domestic homicide and femicide. Overall, 1370 records of homicides among which 236 domestic homicides were identified. The pattern emerging from the statistical results of the present study highlighted the phenomenon of femicide as the gravest current issue to be interpreted and addressed. Nationally, the average number of homicides was 114.2/year, among which 19.7 domestic homicides. However, in 2021, while a decrease was recorded in homicides in general to 89 incidents per year, domestic homicides skyrocketed to 34 cases, reaching the highest annual number ever nationally recorded. On average, domestic homicides account for 18.2% of all homicides in Greece. In 2021, however, this percentage rose to 38.2%. The number of male victims of domestic homicide has declined over the years, with a further decline in 2021, in stark contrast to the number of women escalating over time and even more sharply in 2021. The proportion of female victims of domestic homicides in Greece was fourfold higher on average. The fact that cases of domestic homicide and femicide have received a lot of media attention, the recent Greek financial crisis, as well as increased alcohol and drug consumption due to the COVID-19 pandemic constitute possible aggravating factors.

Effective Prediction and Important Counseling Experience for Perceived Helpfulness of Social Question and Answering-Based Online Counseling: An Explainable Machine Learning Model

The social question answering based online counseling (SQA-OC) is easy access for people seeking professional mental health information and service, has become the crucial pre-consultation and application stage toward online counseling. However, there is a lack of efforts to evaluate and explain the counselors' service quality in such an asynchronous online questioning and answering (QA) format efficiently. This study applied the notion of perceived helpfulness as a public's perception of counselors' service quality in SQA-OC, used computational linguistic and explainable machine learning (XML) methods suited for large-scale QA discourse analysis to build an predictive model, explored how various sources and types of linguistic cues [i.e., Linguistic Inquiry and Word Count (LIWC), topic consistency, linguistic style similarity, emotional similarity] contributed to the perceived helpfulness. Results show that linguistic cues from counselees, counselors, and synchrony between them are important predictors, the linguistic cues and XML can effectively predict and explain the perceived usefulness of SQA-OC, and support operational decision-making for counselors. Five helpful counseling experiences including linguistic styles of "talkative", "empathy", "thoughtful", "concise with distance", and "friendliness and confident" were identified in the SQA-OC. The paper proposed a method to evaluate the perceived helpfulness of SQA-OC service automatically, effectively, and explainable, shedding light on the understanding of the SQA-OC service outcome and the design of a better mechanism for SQA-OC systems.

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Online mental health service (OMHS) has been named as the best psychological assistance measure during the COVID-19 pandemic. An interpretable, accurate, and early prediction for the demand of OMHS is crucial to local governments and organizations which need to allocate and make the decision in mental health resources. The present study aimed to investigate the influence of the COVID-19 pandemic on the online psychological help-seeking (OPHS) behavior in the OMHS, then propose a machine learning model to predict and interpret the OPHS number in advance. The data was crawled from two Chinese OMHS platforms. Linguistic inquiry and word count (LIWC), neural embedding-based topic modeling, and time series analysis were utilized to build time series feature sets with lagging one, three, seven, and 14 days. Correlation analysis was used to examine the impact of COVID-19 on OPHS behaviors across different OMHS platforms. Machine learning algorithms and Shapley additive explanation (SHAP) were used to build the prediction. The result showed that the massive growth of OPHS behavior during the COVID-19 pandemic was a common phenomenon. The predictive model based on random forest (RF) and feature sets containing temporal features of the OPHS number, mental health topics, LIWC, and COVID-19 cases achieved the best performance. Temporal features of the OPHS number showed the biggest positive and negative predictive power. The topic features had incremental effects on performance of the prediction across different lag days and were more suitable for OPHS prediction compared to the LIWC features. The interpretable model showed that the increase in the OPHS behaviors was impacted by the cumulative confirmed cases and cumulative deaths, while it was not sensitive in the new confirmed cases or new deaths. The present study was the first to predict the demand for OMHS using machine learning during the COVID-19 pandemic. This study suggests an interpretable machine learning method that can facilitate quick, early, and interpretable prediction of the OPHS behavior and to support the operational decision-making; it also demonstrated the power of utilizing the OMHS platforms as an always-on data source to obtain a high-resolution timeline and real-time prediction of the psychological response of the online public.