The 7th International Workshop on
Natural Language Processing for Social Media
In conjunction with IJCAI 2019 @ August 12, 2019, Macau, China.


  • The program is announced. Please check here. (2019.07.03)
  • The SocialNLP workshop is scheduled to be in the morning 8:30-12:30 at August 12, 2019. (2019.06.11)
  • Due to the extension of paper submission deadline, the notification date is extended to May 17, 2019. (2019.05.13)
  • The deadline is extended to April 29, 2019. (2019.04.21)
  • The shared task of SocialNLP @ IJCAI-2019, named EmotionX, is announced, please visit the website: (2019.04)
    EmotionX is an emotion detection task on dialogues in the EmotionLines dataset. Participants will need to detect the emotion for each utterance among four label candidates: joy, sadness, anger, and neutral. There will be a special session in the workshop for the EmotionX task. Please visit the challenge website EmotionX for the details.
    March 26, 2019: Registration Open, Data Available
    May 14, 2019: Test Data Released
    May 16, 2019: Labeled Test Data Due
    May 23, 2019: Evaluation Results Released
    May 28, 2019: Draft Technical Report Due
    June 2, 2019: Acceptance and Presentation Mode Notification
    June 9, 2019: Final Technical Report Due

  • To submit your paper, please make the submission via Easychair system. (2019.04.12)
  • The deadline is extended to April 22, 2019. (2019.04.12)
  • Our workshop allows double submission. For double submission, please note the other venue below your paper title. Authors will need to select one venue for publication upon acceptance.
  • SocialNLP 2019 webpage is online. (2019.03.12)

Workshop Description

With social media services' rise of popularity, including general-purpose Microblogs such as Facebook, Twitter, and Plurk, goal-oriented services such as Linkedln (for professional occupation), (a social bookmarking service), and Foursquare (a check-in service for mobile devices), and Web 2.0-based large-scale knowledgebase such as Wikipedia and common-sense corpus, now researchers can assess heterogeneous information of the target human/object that includes not only text content but also meta-data, or even the social relationships among persons.

Furthermore, the content on social media and Web 2.0 platforms is different from that on others in terms of style, tone, purpose, etc. For instance, posts on twitter are limited in size, thus can contain jargons, emoticons, or abbreviations which usually do not follow formal grammar. It is not suitable to apply existing natural language techniques on such content because they are not tailored to do so. For instance, standard summarization techniques might not be suitable for Plurk posts that are relatively short and contain responses from multiple friends; and sentiment dictionaries learned from news corpus might not be suitable for sentiment detection tasks on Microblogs.

As it is generally believed social media has become one of the major means for communication and content producing, while such trend is not likely to fade away, being able to process content from social media platforms does bring a lot of values in real-world applications. Furthermore, due to the change of the style to the content and the availability of heterogeneous resources (e.g. social relationship among people) one can obtain, novel NLP techniques that are designed specifically for such platform and can potentially integrate or learn information from different sources are highly demanded. Below we highlight some (non-exclusive) important themes in this direction.

Organizing the SocialNLP workshop in IJCAI 2019 is three-fold. First, social media analytics is the research topic which is closely related to natural language processing. But with the challenges mentioned above, we resort to the AI community and attempt to find the role of AI/NLP/WWW techniques in SocialNLP. In recent NLP-related conferences, no matter to tell from the number of submissions or participants, it is apparent that sentiment analysis and the social media analytics are certainly two of the main research topics. Second, social media data is essentially generated and collected from online social services, which have accumulated a large number of user-generated social data, i.e., big social data. Processing such big social data with linguistic knowledge and NLP techniques has encountered many important research problems. Through SocialNLP, the cutting edge technology will be introduced to AI researchers, where they might find some inspirations and useful information. Moreover, as SocialNLP has an aim to make data available to the research community and will provide a platform for researchers to share datasets, AI researchers and NLP researchers can get familiar with the data from each other and access them easily. Third, user-generated content in social media is mainly in the form of text. Theories and techniques on artificial intelligence and natural language processing are desired for semantic understanding, accurate search, and efficient processing of social media contents. From the perspective of application, novel online applications involving social media analytics and sentiment analysis, such as emergency management, social recommendation, user behavior analysis, user social community analysis and future prediction, are topics that NLP and AI researchers have paid attention to. In short, hosting SocialNLP workshop in IJCAI will provide mutually-reinforced benefits for researchers in areas of AI techniques, natural language processing and social media analytics. We believe collecting thoughts and comments of these researchers will also bring up many great ideas and opportunities for future research collaborations.

Topics of Interest

Topics of interests for the workshop include, but are not limited to:

Content analysis on Social Media

  • Concept-level sentiment analysis
  • Summarization of posts/replies on social media
  • Name entity Recognition on Social media
  • Relationship extraction on social media
  • Entity resolution for social media
  • Search, Indexing, and Evaluation on Social Web
  • Improving Speech Recognition using Social Media Content
  • Multilingual and Language specific Information Retrieval on Social Web

Natural language processing on Web 2.0

  • Folksonomy and Social Tagging
  • Trend analysis on Wikipedia
  • Trustworthiness analysis on Wikipedia
  • Human computing for social-media corpus generation
  • Social structure and position analysis using Microblog content
  • Trust and Privacy analysis in social contexts
  • Community detection using blogs or Microblog content

Sentiment and Opinion Analysis on Social Media

  • Big social data analysis
  • Lexical semantic resources, corpora and annotations of social media for sentiment analysis
  • Opinion retrieval, extraction, classification, tracking and summarization
  • Domain specific sentiment analysis and model adaptation Emotion detection
  • Sentiment analysis for automatic public opinion poll and surveys of user satisfaction
  • Improvement of NLP tasks using subjectivity and/or sentiment analysis on social platform
  • Sentiment analysis and human computer interface on social platform
  • Real-world sentiment applications and systems on social platform

Disaster Management Using Social Media

  • Modeling global events or human activities based on social media texts
  • Identification and geo-location of social media content
  • Social-based web platform for disaster management
  • Disaster or disease prediction and forecasting
  • Resource allocation using social media
  • Monitoring emergency responses among social crowds
  • Analyzing the diffusion of emergent information
  • Exploiting social media for crisis response and search and rescue activities

Models and Tools Development for SocialNLP

  • Biologically-inspired opinion mining
  • Social-network motivated methods or tools for natural language processing
  • Advanced topic model for social media
  • Learning to rank for social media
  • Clustering and Classification tools for Social Media
  • Content-based and social-based Recommendation
  • Multi-lingual machine translation on Microblog

Paper Submission

SocialNLP @ IJCAI 2019
Page Limit Regular Paper: 7 pages
DATA Paper: 4 pages
(both include references)
Paper Template IJCAI Authors Kit
Submission Site Easychair Submission System

SocialNLP review is double-blind. Therefore, please anonymize your submission: do not put the au-thor(s) names or affiliation(s) at the start of the paper, and do not include funding or other acknowl-edgments in papers submitted for review. In addition to regular paper, we call for DATA PAPER this year. A data paper should include the details of the created dataset and an experiment illustrating how to use it. Authors should note it as a data paper using the author field and submit at least partial data as accompanied materials. The created dataset should be able to be downloaded or acquired through an application process freely. If the data paper is accepted, we will list the link for accessing the dataset in the SocialNLP website. Note that the review for data papers is also double-blind and it is authors' re-sponsibility to avoid revealing their identities.

Papers submitted to this workshop must not have been accepted for publication elsewhere or be under review for another workshop, conference or journal. Papers should be written in English. Each submis-sion will be evaluated by at least 3 program committee members.

To pursue high quality submission, we will have a best paper award of SocialNLP 2019 for both venues. The selection process will depend on not only the review comments/ratings, but also the quality of paper that is rated by paper authors. Selected, expanded versions of papers presented at the workshop will be published in two follow-on Special Issues of Springer Journal of Information Science and Engineering (JISE) and the International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP).


Please note that at least one registration per paper published is required. At the time of submission of the final camera-ready copy, authors will have to indicate the already registered person for that publication.


Date: August 12 morning
Location: TBA

SocialNLP @ IJCAI 2019
08:30 Opening and Welcome
08:30-09:40 [Keynote Speech] Emotion Recognition in Conversation: Research Challenges, Negative Results, and Benchmarks
Soujanya Poria, Assistant Professor, Computer Science at the Singapore University of Technology and Design (SUTD)
Emotion is intrinsic to humans and they express emotion through language, be it verbal or physical. Thus, emotion emulation is a key part of human-like artificial intelligence (AI). Emotion Recognition in Conversation (ERC) is increasingly becoming popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms like Facebook, Youtube, Reddit, Twitter, etc. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), human resources (evaluation of prospective employees) and much more. Besides, ERC is also extremely important for generating empathetic dialogues which requires an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this talk, I will discuss these challenges and as well as shed light on the recent advances in this research field. Finally, I will also present a few negative results that we got while computationally addressing some of these challenges.
Soujanya Poria is an assistant professor of Computer Science at the Singapore University of Technology and Design (SUTD), Singapore. He holds a Ph.D. degree in Computer Science from the University of Stirling, UK. He is a recipient of the prestigious early career research award called 'NTU Presidential Postdoctoral Fellowship' in 2018 which offers him a research grant worth US$150,000. Before taking up his presidential fellowship position at the NTU, Soujanya was a scientist at the A*STAR and the Temasek Laboratory, NTU. He is also (co-)PI of multiple academic and industrial grants with the amount totaling to US$ 250,000. Soujanya has co-authored more than 70 papers, published in top-tier conferences and journals such as ACL, EMNLP, AAAI, NAACL, Neurocomputing, Computational Intelligence Magazine, etc.. He is also an adjunct faculty at Indraprastha Institute of Information Technology, Delhi, India and an adjunct scientist at A*STAR, Singapore. Soujanya served as a senior PC member at AAAI 2019, IJCAI 2019 and often serve as a PC member in reputed conferences such as ACL, EMNLP, IJCAI, NAACL. He was an area co-chair at NAACL 2019, EMNLP 2019 and a publicity chair at *SEM 2019. Soujanya has given several invited talks at venues like CICLing 2018 which is a large international NLP conference. Soujanya has Google Scholar citations of more than 4000 and his h-index is 35. Three of Soujanya's papers are listed as Web of Science highly cited papers in the field of Computer Science. Recently, in an article published in the Journal of Information Sciences, Soujanya has been listed as one of the most prolific and impactful researchers from 2000 to 2016 in the field of sentiment analysis.
Paper Session
[15 Minutes] Bursty Event Detection on Social Media
Yuanjing Cai, Yunli Wang, Samuel Larkin and Cyril Goutte.
[15 Minutes] Ex-Twit: Explainable Twitter Mining on Health Data
Tunazzina Islam.
[15 Minutes] Analysis of Gujarat Assembly Elections 2017 through Micro-Blog: Twitter
Nitesh Rai, Durga Toshniwal and Manoj Misra.
10:30-11:00 Coffee Break
EmotionX Session
Introduction to EmotionX
Boaz Shmueli and Lun-Wei Ku
[15 Minutes] Team 1: NTHU IDEA
EmotionX-IDEA: Emotion BERT-an Affectional Model for Conversation
Yen Hao Huang, Ssu-Rui Lee, Mau-Yun Ma, Yi Hsin Chen, Ya-Wen Yu and Yi-Shin Chen
[15 Minutes] Team 2: KU + KAKAO
EmotionX-KU: BERT-Max based Contextual Emotion Classifier
Kisu Yang, Dongyub Lee, Taesun Whang Seolhwa Lee and Heuiseok Lim
[8 Minutes] Team 4: AlexU
EmotionX-AlexU: Cascade bert-based emotion classifier
Meena Alfons, Marwan Torki, and Nagwa El-Makky
[8 Minutes] Team 6: Podlab
EmotionX-Podlab: A linear SVM for emotion classification.
Andrew Nguyen and Lewis Mitchell
[8 Minutes] Team 7: CYUT
Shih-Hung Wu and Sheng-Lun Chien
EmotionX-CYUT: Attention RNN model with least context for emotion detection in dialogues

Important Dates

SocialNLP @ IJCAI 2019
Submission Deadline April 12, 2019
April 29, 2019
Author Notification May 10, 2019
May 17, 2019
Camera-ready Submission May 24, 2019
Workshop Date August 12

Program Committee

  • Sabine Bergler, Concordia University
  • Berlin Chen, National Taiwan Normal University
  • Hsin-Hsi Chen, National Taiwan University
  • Hai Leong Chieu, DSO National Laboratories
  • Monojit Choudhury, Microsoft Research
  • Freddy Chua, Singapore Management University
  • Nigel Collier, University of Cambridge
  • Danilo Croce, University of Roma, Tor Vergata
  • Lei Cui, Microsoft Research
  • Ronan Cummins, University of Cambridge
  • Pradipto Das, Rakuten USA
  • Min-Yuh Day, Tamkang University, Taiwan
  • Ann Devitt, Trinity College Dublin
  • Eduard Dragut, Temple University
  • Koji Eguchi, Kobe University
  • Michael Elhadad, Ben-Gurion University
  • Wei Gao, Victoria University of Wellington
  • Spandana Gella, University of Edinburgh
  • Marco Guerini, Fondazione Bruno Kessler
  • Weiwei Guo, LinkedIn
  • William Hamilton, Stanford University
  • Graeme Hirst, University of Toronto
  • Wen-Lian Hsu, Academia Sinica
  • Diana Inkpen, University of Ottawa
  • David Jurgens, Stanford University
  • Pallika Kanani, Oracle Labs
  • Soo-Min Kim, Amazon
  • Roman Klinger, University of Stuttgart
  • June-Jei Kuo, National Chung Hsing University
  • Tsung-Ting Kuo, University of California, San Diego
  • Cheng-Te Li, National Cheng Kung University
  • Chuan-Jie Lin, National Taiwan Ocean University
  • Shou-De Lin, National Taiwan University
  • Yiqun Liu, Tsinghua University
  • Zhiyuan Liu, Tsinghua University
  • Bin Lu, Google Inc.
  • Zhunchen Luo, China Defense Science and
  • Technology Information Center
  • Bruno Martins, University of Lisbon
  • Yelena Mejova, Qatar Computing Research Institute
  • Rada Mihalcea, University of Michigan
  • Manuel Montes-y-Gómez, INAOE, Mexico
  • Dong Nguyen, University of Twente
  • Haris Papageorgiou, ATHENA Research and Innovation Center
  • Souneil Park, Telefonica Research
  • Michael Paul, University of Colorado Boulder
  • Georgios Petasis, NCSR "Demokritos"
  • Stephen Pulman, Oxford University
  • Sravana Reddy, Wellesley College
  • Paolo Rosso, Universitat Politècnica de València
  • Derek Ruths, McGill University
  • Saurav Sahay, Intel Labs
  • Hassan Saif, The Open University
  • Scott Nowson, Accenture Centre for Innovation, Dublin, Ireland
  • Yohei Seki, University of Tsukuba
  • Mário J. Silva, Universidade de Lisboa
  • Yanchuan Sim, Institute for Infocomm Research
  • Jan Snajder, University of Zagreb
  • Jannik Strötgen, Max Planck Institute for Informatics
  • Xavier Tannier, Université Paris-Sud, LIMSI, CNRS
  • Mike Thelwall, University of Wolverhampton
  • Ming-Feng Tsai, National Chengchi University
  • Paola Velardi, Università di Roma
  • Marc Verhagen, Brandeis University Svitlana Volkova, PNNL
  • Xiaojun Wan, Peking University
  • Hsin-Min Wang, Academia Sinica
  • Jenq-Haur Wang, National Taipei University of Technology
  • William Yang Wang, UC Santa Barbara
  • Ingmar Weber, Qatar Computing Research Institute, HBKU
  • Robert West, École Polytechnique Fédérale de Lausanne
  • Kam-Fai Wong, The Chinese University of Hong Kong
  • Shih-Hung Wu, Chaoyang University of Technology
  • Ruifeng Xu, Harbin Institute of Technology
  • Yi Yang, Georgia Tech
  • Liang-Chih Yu, Yuan Ze University
  • Zhe Zhang, IBM Watson
  • Hua-Ping Zhang, Beijing Institute of Technology
  • Ming Zhou, Microsoft Research Asia
  • Deyu Zhou, Southeast University


Past SocialNLP


If you are considering submitting to the workshop and have questions regarding the workshop scope or need further information, please do not hesitate to send e-mail to both lwku [AT] and chengte [AT] Thanks!