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 EACL 2017 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 machine learning (ML) community and attempt to find the role of ML 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 EACL. 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 ML 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, ML 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 ML researchers have paid attention to. In short, hosting SocialNLP workshop in EACL will provide mutually-reinforced benefits for researchers in areas of ML 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 interests for the workshop include, but are not limited to:
Content analysis on Social Media
Natural language processing on Web 2.0
Sentiment and Opinion Analysis on Social Media
Disaster Management Using Social Media
Models and Tools Development for SocialNLP
Lun-Wei Ku, Academia Sinica, Taiwan
Cheng-Te Li, National Cheng Kung University, Taiwan
