Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms

Detección automática de casos urgentes en foro de salud mental

Los foros orientados a temas de salud mental necesitan la supervisión de moderadores para brindar apoyo en casos delicados, como mensajes que expresan ideas suicidas. A medida que aumenta el tamaño del foro, la supervisión del moderador deja de ser factible sin la ayuda de sistemas automatizados de priorización. En el presente artículo presentamos un sistema de aprendizaje automático supervisado para el triaje (clasificación según severidad) de mensajes de un foro de salud mental. Este sistema fue desarrollado en el contexto de la competencia CLPsych 2017 shared task y sus resultados serán publicados en los Proceedings of the CLPsych 2018 workshop. El triaje consiste en clasificar cada mensaje de un foro de salud mental según la necesidad de intervención. Los niveles del triaje son: crisis, red, amber y green reflejando una prioridad decreciente en la atención de los moderadores del foro. El dataset de la competencia contiene 146030 mensajes sin etiquetar y 1588 mensajes etiquetados por especialistas: 1188 mensajes como training set y 400 mensajes como test set. Esta competencia fue una extensión de la realizada en el 2016. La mayoría de los enfoques en la literatura se centran en el contenido de los mensajes, pero sólo unos pocos autores aprovechan las atributos contextuales. En nuestro trabajo aplicamos un enfoque novedoso teniendo en cuenta no sólo atributos capaces de captar el contenido del mensaje sino también el contexto en el que se producen, considerando el historial de mensajes y la red de interacciones.Sociedad Argentina de Informática e Investigación Operativ

Detección automática de casos urgentes en foro de salud mental

Los foros orientados a temas de salud mental necesitan la supervisión de moderadores para brindar apoyo en casos delicados, como mensajes que expresan ideas suicidas. A medida que aumenta el tamaño del foro, la supervisión del moderador deja de ser factible sin la ayuda de sistemas automatizados de priorización. En el presente artículo presentamos un sistema de aprendizaje automático supervisado para el triaje (clasificación según severidad) de mensajes de un foro de salud mental. Este sistema fue desarrollado en el contexto de la competencia CLPsych 2017 shared task y sus resultados serán publicados en los Proceedings of the CLPsych 2018 workshop. El triaje consiste en clasificar cada mensaje de un foro de salud mental según la necesidad de intervención. Los niveles del triaje son: crisis, red, amber y green reflejando una prioridad decreciente en la atención de los moderadores del foro. El dataset de la competencia contiene 146030 mensajes sin etiquetar y 1588 mensajes etiquetados por especialistas: 1188 mensajes como training set y 400 mensajes como test set. Esta competencia fue una extensión de la realizada en el 2016. La mayoría de los enfoques en la literatura se centran en el contenido de los mensajes, pero sólo unos pocos autores aprovechan las atributos contextuales. En nuestro trabajo aplicamos un enfoque novedoso teniendo en cuenta no sólo atributos capaces de captar el contenido del mensaje sino también el contexto en el que se producen, considerando el historial de mensajes y la red de interacciones.Sociedad Argentina de Informática e Investigación Operativ

Evaluating an online well-being program for college students during the COVID-19 pandemic

Introduction: The global COVID-19 pandemic has aggravated challenges involving college students’ mental health and well-being. Some literature suggested developing online programs to address the pandemic’s impact on college students’ mental health and well-being. Thus, this study assessed if significant improvement in well-being among college students can be observed after introducing an online well-being program.Methods: The study utilized a quantitative methodology, mainly using a two-group pretest-posttest design on 178 college students in a private college and state university. The experimental group received 3 months of the well-being program while the control resumed their activities of daily living (ADL). The modified positive emotion, engagement, relationship, meaning, and accomplishment (PERMA) profiler questionnaire was the primary evaluation instrument that measured the participants’ well-being. The first phase gathered the participants’ relevant profile and background, and the last phase concluded with the evaluation of the program. Data were analyzed using SPSS v.21.Results: Based on the post-evaluation PERMA scores, the experimental participants (M = 7.21, SD 1.70) did not differ much from the control (M = 7.07, SD = 1.55) according to a t-test t(176) = –1.07, p = 0.57 as computed using a two-sample independent t-test at a significance level of α = 0.05. The overall PERMA score description is normal functioning. The Pearson correlation of the experimental group’s pre-test and post-test scores (r(91) = 0.01, p = 0.904) and the control (r(83) = 0.04, p = 0.732) group did not indicate an evidence of a significant relationship.Conclusion: The results do not provide evidence of a significant difference and relationship between the experimental participants’ pre-test and post-test PERMA scores after the online well-being program

Analysis of Users’ Sentiments in Social Media (on the Example of the Astrakhan Region)

The article is devoted to the studying of the opinions and sentiments of users of regional communities in the social network VKontakte using methods of machine analysis of text data, supplemented by sociological research methods. In the course of the study, we identified a list of current topics discussed by the inhabitants of the region, determined the most frequently mentioned persons, and analyzed the tone of their mention. Additionally, on the basis of the obtained results, the index of subjective (non-) well-being (ISW) was calculated for each district of the region and a map of the emotional coloring of posts from the communities of the analyzed social network was built. The results of the study can be used to monitor the situation in the region, finding problem areas, elicitation opinion leaders (popular personalities of the region that have a special influence on the opinion of the population), as well as identify the most interesting topics and urgent problems for the population. In perspective, this method of monitoring the social sentiments of the population of the region can be improved by automating the addition of new data to the analytical project. In the future, the addition of mathematical models to the system will make it possible to create graphs for predicting further changes in the region

Lightme: Analysing Language in Internet Support Groups for Mental Health

Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Methods: Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout mental health forum for young people. Results: When comparing with the state-of-the-art, a solution mainly based on features from lexical resources, received the best classification performance for the crisis posts (52%), which is the most severe class. Six salient linguistic characteristics were found when analysing the crisis post; 1) posts expressing hopelessness, 2) short posts expressing concise negative emotional responses, 3) long posts expressing variations of emotions, 4) posts expressing dissatisfaction with available health services, 5) posts utilising storytelling, and 6) posts expressing users seeking advice from peers during a crisis. Conclusion: It is possible to build a competitive triage classifier using features derived only from the textual content of the post. Further research needs to be done in order to translate our quantitative and qualitative findings into features, as it may improve overall performance

Analyzing Happiness: Investigation on Happy Moments using a Bag-of-Words Approach and Related Ethical Discussions

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look to see what other components have a role in making a specific event happy and further analyze how we can classify such words. Also, we dive deeper into specific subcategories of classifiers in an attempt to form insights about their happiness level based on specific factors. With the goal to extract features from the text in HappyDB, in this study we used the bag of words approach. Through doing so, our results were successful at predicting the happiness category, concerning both accuracy and context. Our models were able to accomplish the goal of understanding a happy moment and fit such a moment into one of the seven ground truth happiness categories we set at the beginning of this study. We finished the article with the ethical perspective of such research works and related social implications