MentalRiskES corpus

MentalRiskES is a new dataset about mental disorders in Spanish. The dataset is divided into three distinct mental disorders: Eating Disorder Depression Anxiety Each dataset contains a set of subjects and their message thread in a Telegram social network chat. How is constructed?Public groups on the Telegram social network were accessed, and conversations were extracted from them. This data was processed, and we kept only the text messages, excluding images, audio, etc. In order to carry out the annotation, a subset of messages was extracted from each subject. This message thread was annotated by 10 different annotators through the Prolific platform and made use of the Doccano annotation platform. In this way, we associated a user ID with some tags that emerged after averaging the annotators' decisions. The labels available for each set are: Eating Disorder: suffer (s), control (c) Depression: suffer + in favour (sf), suffer + against (sa), suffer + other (so), control (c) Anxiety: suffer (s), control (c) LabelsThe values available in Anxiety files are: bs (binary suffer): 1 if the subject suffers and 0 if not according to the frequency of the labels (in case of a tie it is marked as suffers) bc (binary control): 1 if the subject does not suffer and 0 if they do according to the frequency of the labels (in case of a tie it is marked as suffers) rbs (regression binary suffer): number of times the subject has been marked as suffering among the total number of scorers, i.e., 10 rbc (regression binary control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10 The values available in the Depression and Eating Disorders files are: bs (binary suffer): 1 if the subject suffers and 0 if not, according to the frequency of the labels (in case of a tie it is marked as suffers) bsf (binary suffer favour): 1 if the subject suffers and is in favour,r and 0 if not according to the frequency of the labels bsa (binary suffer against): 1 if the subject suffers and is against, and 0 if not according to the frequency of the labels bso (binary suffer other): 1 if the subject suffers and is neither in favour nor against and 0 if not according to the frequency of the labels bc (binary control): 1 if the subject does not suffer and 0 if they do according to the frequency of the labels (in case of a tie it is marked as suffers) rbs (regression binary suffer): number of times the subject has been marked as suffering among the total number of scorers, i.e., 10 rbc (regression binary control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10 rsf (regression suffer favour): number of times the subject has been marked as suffering and in favour among the total number of scorers, i.e., 10 rsa (regression suffer against): number of times the subject has been marked as suffering and against and in favour among the total number of scorers, i.e., 10 rso (regression suffer other): number of times the subject has been marked as suffering and is neither in favour nor against among the total number of scorers, i.e., 10 rc (regression control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10 (Note that it is equal to 'rbc')So, the labels 'rbs' and 'rbc' must sum to 1, and the labels 'rsf','rsa', 'rso' and 'rc' must sum to 1 too. PreprocessingThe same corpus is found with emojis or without emojis; that is to say, in the folder 'processed' is the corpus with emojis in text format, while in the folder 'raw' is the corpus with emojis in original format. MentalRiskES evaluation campaignMentalRiskES is a shared task organized at IberLEF. The aim of this task is to promote the early detection of mental risk disorders in Spanish. In this task we made use of the corpusMentalRiskES, the partitions used are available in the folder MentalRiskES2023edition.zip provided in git (https://github.com/sinai-uja/corpusMentalRiskES). To cite the task: Mármol-Romero, A. M., Moreno-Muñoz, A., Plaza-del-Arco, F. M., Molina-González, M. D., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2023). Overview of MentalriskES at IberLEF 2023: Early Detection of Mental Disorders Risk in Spanish. Procesamiento del Lenguaje Natural, 71, 329-350.MentalRiskES是一款面向西班牙语的全新精神障碍数据集。该数据集涵盖三类明确的精神障碍：进食障碍、抑郁症、焦虑症。 各子数据集均收录了Telegram社交网络聊天中的受试者及其消息会话线程。 ### 数据集构建方式 我们访问了Telegram社交网络的公开群组，并从中提取对话内容。随后对数据进行预处理，仅保留文本消息，剔除图片、音频等非文本内容。为完成标注工作，我们从每位受试者的消息中抽取了一个子集，该消息子集由10名来自Prolific平台的标注员通过Doccano标注平台完成标注。 最终，我们将用户ID与经标注员决策结果取平均后得到的标签进行关联。各子数据集可用的标签如下： - 进食障碍：患病（s）、管控（c） - 抑郁症：患病+支持（sf）、患病+反对（sa）、患病+其他（so）、管控（c） - 焦虑症：患病（s）、管控（c） ### 标签说明 焦虑症相关文件中的可用标签值如下： - bs（二元患病标签）：根据标签投票频率判定，若受试者患病则取值为1，未患病则为0；若出现平局，则判定为患病。 - bc（二元管控标签）：根据标签投票频率判定，若受试者未患病则取值为1，患病则为0；若出现平局，则判定为患病。 - rbs（回归式二元患病标签）：受试者被标注为患病的次数占总标注次数（共10次）的数值（即标注次数）。 - rbc（回归式二元管控标签）：受试者被标注为未患病的次数占总标注次数（共10次）的数值（即标注次数）。 抑郁症与进食障碍相关文件中的可用标签值如下： - bs（二元患病标签）：根据标签投票频率判定，若受试者患病则取值为1，未患病则为0；若出现平局，则判定为患病。 - bsf（二元患病+支持标签）：根据标签投票频率判定，若受试者患病且持支持立场则取值为1，否则为0。 - bsa（二元患病+反对标签）：根据标签投票频率判定，若受试者患病且持反对立场则取值为1，否则为0。 - bso（二元患病+其他标签）：根据标签投票频率判定，若受试者患病且既不支持也不反对则取值为1，否则为0。 - bc（二元管控标签）：根据标签投票频率判定，若受试者未患病则取值为1，患病则为0；若出现平局，则判定为患病。 - rbs（回归式二元患病标签）：受试者被标注为患病的次数占总标注次数（共10次）的数值（即标注次数）。 - rbc（回归式二元管控标签）：受试者被标注为未患病的次数占总标注次数（共10次）的数值（即标注次数）。 - rsf（回归式患病+支持标签）：受试者被标注为患病且持支持立场的次数占总标注次数（共10次）的数值（即标注次数）。 - rsa（回归式患病+反对标签）：受试者被标注为患病且持反对立场的次数占总标注次数（共10次）的数值（即标注次数）。 - rso（回归式患病+其他标签）：受试者被标注为患病且既不支持也不反对的次数占总标注次数（共10次）的数值（即标注次数）。 - rc（回归式管控标签）：受试者被标注为未患病的次数占总标注次数（共10次）的数值（即标注次数）（注：该标签与rbc等价）。 因此，rbs与rbc的取值之和必须为1，rsf、rsa、rso与rc的取值之和同样必须为1。 ### 预处理 本数据集提供两种版本的语料：保留表情符号的版本与移除表情符号的版本。其中，`processed`文件夹中为文本格式且保留了原始表情符号的语料，而`raw`文件夹中为保留了原始格式表情符号的语料。 ### MentalRiskES评估活动 MentalRiskES是在IberLEF上举办的一项共享任务，旨在推动西班牙语精神风险障碍的早期检测。本次任务采用了MentalRiskES语料库，所用的数据划分可在GitHub仓库https://github.com/sinai-uja/corpusMentalRiskES提供的`MentalRiskES2023edition.zip`文件中获取。该任务的引用格式为：Mármol-Romero, A. M., Moreno-Muñoz, A., Plaza-del-Arco, F. M., Molina-González, M. D., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2023). Overview of MentalriskES at IberLEF 2023: Early Detection of Mental Disorders Risk in Spanish. Procesamiento del Lenguaje Natural, 71, 329-350.