SocialNLP 2016 - 4th International Workshop on Natural Language Processing for Social Media

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), Del.icio.us (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 2016 and EMNLP 2016 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 and NLP 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 both IJCAI and EMNLP. 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 both IJCAI and EMNLP 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