The 6th International Workshop on
Natural Language Processing for Social Media
In conjunction with ACL 2018 @ July 20, 2018, Melbourne, Australia.
In conjunction with WWW 2018 @ April 23, 2018, Lyon, France.


  • The locatino/room of SocialNLP @ ACL-2018 is "Room 216, MCEC", and the starting time is 9:20 AM. (2018.07)
  • The program of SocialNLP @ ACL-2018 is announced. Please refer to Program @ ACL. (2018.07)

  • The Camera-Ready submission of SocialNLP @ ACL-2018 is postponed to May 30, 2018. (2018.05.21)
  • Due to the extended submission deadline, the author notification of SocialNLP @ ACL-2018 is postponed to May 20, 2018. (2018.05.08)
  • The submission deadline of SocialNLP @ ACL-2018 is extended to April 24, 2018. (2018.04.23)
  • The locatino/room of SocialNLP @ WWW-2018 is "Salle Gratte-Ciel 1", and the starting time is 9:30 AM. (2018.04.21)
  • The program of SocialNLP @ WWW-2018 is announced. Please refer to Program @ WWW. (2018.04.03)

  • The shared task of SocialNLP @ ACL-2018, named EmotionX, is announced, please visit the website: (2018.04.01)
    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. Each team needs to submit a paper describing their system before the task paper submission deadline. This is MANDATORY. After the notification of evaluation, participants can analyze their models and results accordingly and update their paper so as to submit their camera ready version. There will be a special session in the workshop for the EmotionX task. Those teams that achieve the best results or propose novel models will be selected for oral presentation in this session, and others should present their systems with posters. The type of presentation, oral or poster, will be announced together with the evaluation results. Please visit the challenge website EmotionX for the details.
    May 1, 2018: Task Registration Deadline
    May 7, 2018: Task Paper Submission Deadline
    May 14, 2018: Test Set Release
    May 18, 2018: Results Due
    May 21, 2018: Evaluation Notification
    May 28, 2018: Task Paper Camera Ready

  • The submission deadline of SocialNLP @ ACL-2018 has been postponed to April 15, 2018. (2018.04.01)
  • The submission deadline of SocialNLP @ WWW-2018 has been postponed to January 26, 2018. (2018.01.15)
  • SocialNLP 2018 webpage is online. (2017.11.25)

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 ACL 2018 and WWW 2018 is four-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, we have a strong program committee (around 100 researchers) this year, in which 88% members have been reviewers for ACL series of conferences, which are top ones for NLP related research, and they can be very helpful in promoting our workshop. Therefore, we believe that the SocialNLP workshop can draw much interest and attract many audiences from potential academic or industrial participants of NLP. We think such high visibility of SocialNLP can bring more participants and submissions to ACL. Third, 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. Fourth, 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 ACL 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 @ ACL 2018 SocialNLP @ WWW 2018
Page Limit Regular Paper: 8 pages
DATA Paper: 4 pages
(both allow additional 2 pages for references)
Regular Paper: 8 pages
DATA Paper: 5 pages
(both includes references)
Paper Template ACL Style Files ACM Format (sigconf)
Submission Site

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. For SocialNLP@ACL-2018, the workshop proceedings will be published in ACL Anthology.

To pursue high quality submission, we will have a best paper award of SocialNLP 2018 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.

Program @ WWW

Date: April 23
Location: Salle Gratte-Ciel 1 @ Lyon Convention Center

SocialNLP @ WWW 2018
09:30 Opening and Welcome
09:30-10:40 [Keynote Speech] Armed Conflicts in Online News: A Multilingual Study
Robert West, Assistant Professor, School of Computer and Communication Sciences at EPFL
Wars and conflicts have constituted major events throughout history. Despite their importance, the general public typically learns about such events only indirectly, through the lens of news media, which necessarily select and distort events before relaying them to readers. Quantifying these processes is important, as they are fundamental to how we see the world, but the task is difficult, as it requires working with large and representative datasets of unstructured news text in many languages. To address these issues, we propose several simple yet effective unsupervised methods for compiling and analyzing a multilingual corpus of millions of online news documents about armed conflicts. We then apply our methods to answer a number of research questions: First, how widely are armed conflicts covered by online news media across 13 languages, and how does this change as conflicts progress? Second, what role does the level of violence of a conflict play? And third, how well informed is a reader when following a limited number of online news sources? We find that coverage levels are different across conflicts, but similar across languages for a given conflict; that Middle Eastern conflicts receive more attention than African conflicts, even when controlling for the level of violence; and that for most languages and conflicts, following very few sources is enough to stay continuously informed. Finally, given the prominence of conflicts in the Middle East, we further analyze them in a more detailed case study. (Joint work with Jürgen Pfeffer from TU Munchen.)
Since December 2016, Bob has been a tenure-track assistant professor at EPFL in the School of Computer and Communication Sciences, where he heads the Data Science Lab (dlab). His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. He holds a PhD in computer science from Stanford University, as well as a Master's degree from McGill University in Canada and a Diplom from TU Munich in his native Germany.
10:40-11:00 Coffee Break
Paper Session
[20 Minutes] What about Mood Swings? Identifying Depression on Twitter with Temporal Measures of Emotions
Xuetong Chen, Martin Sykora, Thomas Jackson and Suzanne Elayan.
[20 Minutes] Emerging Product Topics Prediction in Social Media without Social Structure Information
Sinya Peng, Vincent Shin-Mu Tseng, Che-Wei Liang and Man-Kwan Shan.
[15 Minutes] Detection of Stress and Relaxation Magnitudes for Tweets
Reshmi Gopalakrishna Pillai, Prof Mike Thelwall and Dr Constantin Orasan.
[15 Minutes] Blurb Mining: Discovering Interesting Excerpts from E-commerce Product Reviews
Saratchandra Indrakanti, Gyanit Singh and Justin House.

Program @ ACL

Date: July 20
Location: Room 216 @ MCEC

SocialNLP @ ACL 2018
09:20-10:30 Keynote Speech (I):The Search for Emotions, Creativity, and Fairness in Language
Dr. Saif Mohammad (NSF)
10:30-11:00 Coffee Break
Technical Session 1
Sociolinguistic Corpus of WhatsApp Chats in Spanish among College Students
Alejandro Dorantes, Gerardo Sierra, Tlauhlia Yamín Donohue Pérez, Gemma BelEnguix, Mónica Jasso Rosales.
A Crowd-Annotated Spanish Corpus for Humor Analysis
Santiago Castro, Luis Chiruzzo, Aiala Rosá, Diego Garat, Guillermo Moncecchi.
A Twitter Corpus for Hindi-English Code Mixed POS Tagging
Kushagra Singh, Indira Sen, Ponnurangam Kumaraguru.
Detecting Offensive Tweets in Hindi-English Code-Switched Language
Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata.
12:20-13:20 Lunch
13:20-14:30 Keynote Speech (II): Understanding Online Social Behaviors through Automatic Language Analysis
Dr. Yi-Chia Wang (Uber)
EmotionX Challenge Session
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues
Chao-Chun Hsu and Lun-Wei Ku.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
Linkai Luo, Haiqin Yang, Francis Y. L. Chin.
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier
Sopan Khosla.
EmotionX-SmartDubai_NLP: Detecting User Emotions In Social Media Text
Hessa AlBalooshi, Shahram Rahmanian, Rahul Venkatesh Kumar.
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
Rohit Saxena, savita bhat, Niranjan Pedanekar.
EmotionX-[JTML]: Detecting emotions with Attention
Johnny Torres.
15:30-16:00 Coffee Break
Technical Session 2
Towards Automation of Sense-type Identification of Verbs in OntoSenseNet (Telugu)
Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi.
Improving Classification of Twitter Behavior During Hurricane Events
Kevin Stowe, Jennings Anderson, Martha Palmer, Leysia Palen, Ken Anderson.
Political discourse classification in social networks using context sensitive convolutional neural networks
Aritz Bilbao-Jayo and Aitor Almeida.
17:00-17:10 Closing (by Dr. Lun-Wei Ku)

Important Dates

SocialNLP @ ACL 2018 SocialNLP @ WWW 2018
Submission Deadline April 8, 2018
April 15, 2018
April 24, 2018
January 21, 2018
January 26, 2018
Author Notification May 7, 2018
May 20, 2018
February 14, 2018
Camera-ready Submission May 28, 2018
May 30, 2018
March 4, 2018
Workshop Date July 20, 2018 April 23, 2018

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!