Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But while there is strong evidence for socio-demographic characteristics in language, they are infrequently used in Natural Language Processing (NLP). Age and gender are somewhat well represented, but Labov's original target, socioeconomic status, is noticeably absent. And yet it matters. We show empirically that NLP disadvantages less-privileged socioeconomic groups. We annotate a corpus of 95K utterances from movies with social class, ethnicity and geographical language variety and measure the performance of NLP systems on three tasks: language modelling, automatic speech recognition, and grammar error correction. We find significant performance disparities that can be attributed to socioeconomic status as well as ethnicity and geographical differences. With NLP technologies becoming ever more ubiquitous and quotidian, they must accommodate all language varieties to avoid disadvantaging already marginalised groups. We argue for the inclusion of socioeconomic class in future language technologies.
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
ETH Zürich（苏黎世联邦理工学院）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Michigan（密歇根大学）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Allen Institute for AI (AI2)（艾伦人工智能研究所（AI2））
pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks
Instituto de Ciencias de la Computación, CONICET, Universidad de Buenos Aires（布宜诺斯艾利斯大学 CONICET 计算机科学研究所）；Facultad de Astronomía, Matemática y Física, Universidad Nacional de Córdoba（科尔多瓦国立大学天文、数学与物理学院）；IIIA-CSIC（西班牙国家研究委员会人工智能研究所）
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Cardiff University（卡迪夫大学）；Queen Mary University of London（伦敦女王学院）
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；McGill University（麦吉尔大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Connecticut（康涅狄格大学）；Uconn Health（康涅狄格大学健康中心）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）；Veterans Affairs Bedford Healthcare System（退伍军人事务部贝德福德医疗系统）；United States Department of Veterans Affairs（美国退伍军人事务部）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP
IT University of Copenhagen（哥本哈根信息技术大学）；Uppsala University（乌普萨拉大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Google AI Language（谷歌人工智能语言部门）；Canva；Microsoft Research（微软研究院）
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
University of Bucharest（布加勒斯特大学）；PRHLT Research Center（PRHLT研究中心）；Universitat Politècnica de València（瓦伦西亚理工大学）；Rochester Institute of Technology（罗切斯特理工学院）；George Mason University（乔治梅森大学）；Human Language Technology Research Center（人类语言技术研究中心）；Faculty of Mathematics and Computer Science, University of Bucharest（布加勒斯特大学数学与计算机科学学院）
Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model
Weill Cornell Medicine（威尔康奈尔医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Mayo Clinic（梅奥诊所）；Columbia University Vagelos College of Physicians and Surgeons（哥伦比亚大学瓦格洛斯内科医生与外科医生学院）；New York State Psychiatric Institute（纽约州精神病学研究所）
A Case Report On The "A.I. Locked-In Problem": social concerns with modern NLP
Institute for Management and Digitalization IMD（管理与数字化研究所（IMD））；Kalaidos University of Applied Sciences（卡拉多斯应用科学大学）；Laboratory for Cognitive Neuroscience LCNS（认知神经科学实验室（LCNS））；University of Fribourg（弗里堡大学）；Translational Research Center（转化研究中心）；University Hospital for Psychiatry Bern（伯尔尼大学精神病院）
Assessing the Applicability of Natural Language Processing to Traditional Social Science Methodology: A Case Study in Identifying Strategic Signaling Patterns in Presidential Directives
Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case
University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；IBM Research - Almaden（IBM阿尔马登研究中心）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Shaheed Benazir Bhutto Women University（沙希德·贝娜齐尔·布托女子大学）
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
University of Copenhagen（哥本哈根大学）；University of Michigan-Ann Arbor（密歇根大学安娜堡分校）；ETH Zurich（苏黎世联邦理工学院）；University of North Texas（北卡罗来纳州立大学）；Sony Computer Science Laboratories - Paris（索尼计算机科学实验室-巴黎）；Santa Clara University（圣克拉拉大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Dataminr（DataMinr公司）；United Nations Development Programme (UNDP)（联合国开发计划署）；University of Maryland, College Park（马里兰大学学院市分校）；Max Planck Institute for Intelligent Systems, Tübingen（智能系统马克斯·普朗克研究所，图宾根）；Vector Institute（向量研究所）；University of Toronto（多伦多大学）；University of Washington（华盛顿大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；IIT Kharagpur（印度理工学院Kharagpur分校）；University of California San Diego（加州大学圣地亚哥分校）；Princeton University（普林斯顿大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University of Alicante（阿利坎特大学）；University of Michigan-Flint（密歇根大学弗林特分校）；University of Southern California（南加州大学）；Technical University of Munich（慕尼黑技术大学）
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
Texas A&M University, San Antonio（德克萨斯A&M大学圣安东尼奥分校）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；New Jersey Institute of Technology（新泽西理工学院）；Qatar Computing Research Institute（卡塔尔计算研究所）；King Faisal University（费萨尔国王大学）
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Stony Brook University（石溪大学）；JP Morgan Chase（摩根大通）；Bloomberg（彭博公司）；Preferabli（普雷费布利公司）；University of Pennsylvania（宾夕法尼亚大学）；National Institute of Health（美国国立卫生研究院）；Grammarly（grammarly公司）
Speciesism in Natural Language Processing Research
Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究生院）；Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术学院）
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
IIT Kharagpur（印度理工学院Kharagpur分校）；University of Hamburg（汉堡大学）；Technical University of Munich（慕尼黑技术大学）；Bucerius Law School（布勒斯海姆法学院）；Skolkovo Institute of Science and Technology（斯克尔科维科学与技术研究所）；Artificial Intelligence Research Institute（人工智能研究 institute）
