With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons
Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）；Institute of Population Research, Peking University（北京大学人口研究所）；University of Puget Sound（普吉特湾大学）
Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media
IEEE Publication Technology Department（IEEE出版技术部）；Department of Computing（计算系）；The Hong Kong Polytechnic University（香港理工大学）；Department of Computer Science, Technology（计算机科学与技术系）；Tsinghua University（清华大学）；Department of Social and Behavioural Sciences（社会科学与行为科学系）；City University of Hong Kong（香港城市大学）
Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach
Medical University of Vienna（维也纳医科大学）；Complexity Science Hub Vienna（维也纳复杂性科学中心）；Graz University of Technology（格拉茨工业大学）；Institute of Globally Distributed Open Research and Education（全球分布式开放研究与教育研究所）；Vienna University of Technology（维也纳工业大学）
Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Informatics and Networked Systems（信息与网络系统系）；University of Pittsburgh（匹兹堡大学）；Department of Psychiatry（精神病学系）；Tandon School of Engineering（塔能工程学院）；New York University（纽约大学）
SoftlyAI；Kyung Hee University Medical Center（庆熙大学医学中心）；SungKyunKwan University（成均馆大学）；KAIST（韩国科学技术院）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）
Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；New York State Psychiatric Institute（纽约州精神病学研究所）；Kent State University（肯特州立大学）
Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations
BJIT Limited（BJIT有限公司）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
随着时间的推移，有害或滥用的在线内容不断增加，引起了社交媒体平台，政府机构和政策制定者的关注。这种有害或滥用的内容可能对社会产生重大的负面影响，例如，网络欺凌可能导致自杀，关于COVID-19的谣言可能导致疫苗犹豫，推广COVID-19的假治疗可能导致健康损害和死亡。在线发布和共享的内容可以是文本的、视觉的或两者的组合，例如，in a meme模因.在这里，我们描述了我们在检测有害模因中实体（英雄，恶棍，受害者）角色的实验，这是约束-2022共享任务的一部分，以及我们的任务系统。我们进一步提供了不同实验设置的比较分析（即，单峰、多峰、注意力和增强）。为了可重复性，我们公开了我们的实验代码。\url{https://github.com/robi56/harmful_memes_block_fusion}
