The 3rd International Workshop on
Natural Language Processing for Social Media
In conjunction with WWW 2015 @ May 19, 2015, Florence, Italy.
In conjunction with NAACL 2015 @ Jun 05, 2015, Denver, Colorado, USA.

News

  • Workshop programs are announced. (2015.03.31)
  • The notification of SocialNLP@WWW 2015 was postponed to Feb 28, 2015. (2015.02.24)
  • The submission deadline of SocialNLP@NAACL 2015 was extended to Mar 7, 2015. (2015.02.23)
  • The submission deadline of SocialNLP@WWW 2015 was extended to Feb 6, 2015. (2015.01.24)
  • SocialNLP 2015 webpage is online. (2014.12.02)

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), 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 WWW 2015 is three-fold. First, social media data is essentially generated and collected from online social services that are func-tioned based on Web techniques. One can take advantage of the knowledge of Web techniques to investigate various kinds of user behaviors in social media and investigate the interactions between users. Second, user-generated data in social media is mainly in the form of text. Theories and techniques on Web information retrieval and natural lan-guage processing are desired for semantic understanding, accurate search, and efficient processing of big social me-dia data. Third, from the perspective of application, if so-cial media data can be effectively processed to distill the collective knowledge of users, novel Web applications, such as emergency management, social recommendation, and future prediction, can be developed with higher accuracy and better user experience. We expect SocialNLP workshop in WWW community can provide mutually-reinforced benefits for researchers in areas of Web techniques, information retrieval and social media analytics.

On the other hand, we organizing SocialNLP in NAACL 2015 because of the following rationales. First, social media analysis and sentiment analysis are two research topics which are closely related to natu-ral language processing. Moreover, their development highly depends on NLP techniques due to textual data. In recent NAACL/ACL/EMNLP conferences, no matter to tell from the number of submissions or participants, it is apparent that they are certainly two of the biggest research communities. Second, social media data consists of not only social connections but also a plentiful of interaction textual con-tents, such as short messages, comments, and opinions. Processing such big social data with linguistic knowledge and NLP techniques has encountered many important research problems. In short, hosting SocialNLP workshop in NAACL will provide mutually-reinforced benefits for researchers in areas of Web 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 re-search 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

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

Program @ WWW-2015 (Tentative)

Date: 2015.05.19
Location: LORENESE ROOM 4

SocialNLP @ WWW 2015
09:10-09:15 Opening and Welcome
09:15-10:25 [Keynote Speech] Mining Social and Urban Big Data
Nicholas Jing Yuan, Associate Researcher, Microsoft Research
Abstract. In recent years, with the rapid development of positioning, online social networks, sensor and smart device technologies, large quantities of human behavioral data are now readily available. The growing availability of such behavioral data provides us an unprecedented opportunity to gain more in depth understanding of users in both the physical world and cyber world, especially in online social networks. In this talk, I will introduce our recent research efforts in social and urban mining based on large-scale human behavioral datasets showcased by two projects: 1) Modeling urban lifestyle spectrums based on heterogeneous online social network data. 2) Inferring demographic attributes from location check-ins.
Bio. Nicholas (Jing) Yuan is currently an associate researcher in Microsoft Research Asia. He got a Ph.D degree in Computer Science from the School of Computer Science and Technology in 2012, and a B.S. degree in Mathematics from the School of the Gifted Young in 2007, both in University of Science and Technology of China. Currently, his research interests include behavioral data mining, spatial-temporal data mining and computational social science. During the past few years, Nicholas has published a series of papers in top-tier conferences and journals. His work has been featured by influential media such as MIT Technology Review many times. He has been honored with Microsoft Fellowship (2011), Best Paper Award of IEEE International Conference on Data Mining (2013), Best Paper Runner-up Award of ACM SIGSPATIAL (2010), and Top 100 Distinguished Doctoral Dissertation Award of Chinese Academy of Sciences (2013).
10:30-11:00 Coffee Break
11:00-12:30 Supervised Prediction of Social Network Links Using Implicit Sources of Information
Ervin Tasnadi, Gabor Berend
Expert-Guided Contrastive Opinion Summarization for Controversial Issues
Jinlong Guo, Yujie Lu, Tatsunori Mori, Catherine Blake
ResToRinG CaPitaLiZaTion in #TweeTs
Kamel Nebhi, Kalina Bontcheva, Genevieve Gorrell

Program @ NAACL-2015 (Tentative)

Date: 2015.06.05
Location: TBA

SocialNLP @ NAACL 2015
09:20-09:30 Opening and Welcome
09:30-10:30 [Keynote Speech] Variation and Change in Social Media Language
Jacob Eisenstein, Assistant Professor, Georgia Tech
Abstract. Social media is sometimes described as a new domain, genre, or task for natural language processing. This suggests that it has specific properties that distinguish it from other sources of text. I will argue that there are exactly two such properties: variation and change. NLP research has historically focused on genres such as newstext, where there is strong pressure towards standardization. Far less pressure exists in social media, and so we must contend with variation on all levels of the linguistic spectrum. This variation enables authors to mark a diverse array of social relationships and identities, and with this increasingly important interpersonal role, online writing becomes enmeshed in complex social processes that lead to instability and change. The inherently dynamic nature of social media language is why we can no longer annotate our way to high accuracy NLP, so learning from unlabeled data will be increasingly critical. Finally, while variation and change pose challenges, they also offer new opportunities for deepening our understanding of both language and social processes. I will describe our recent work on mining four years of Twitter data to uncover macro-scale pathways of linguistic influence among American cities.
Bio. Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob's research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.
10:30-11:00 Coffee Break
11:00-12:00
NLP Session
Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency
Takashi Awamura, Daisuke Kawahara, Eiji ARAMAKI, Tomohide Shibata, Sadao Kurohashi
Paraphrase Identification and Semantic Similarity in Twitter with Simple Features
Ngoc Phuoc An Vo, Simone Magnolini, Octavian Popescu
12:00-13:30 Lunch
13:30-14:00 Pannel Discussion
14:00-15:00 [Keynote Speech] Predicting Pragmatic Reasoning about Language Use in Context
Michael C. Frank, Associate Professor, Stanford University
Abstract. A short, ambiguous message can convey a lot of information, provided the listener is willing to make inferences based on assumptions about the speaker and the context of the message. These sorts of pragmatic inferences are critical in facilitating efficient human communication, and have been characterized informally using tools like Grice's conversational maxims. In this talk, I'll describe our work on a new, probabilistic framework for referential communication in context. This framework shows good fit to adults' and children's judgments across many experiments, provides extensions to a variety of complex linguistic phenomena, and resolves some important puzzles about language processing. I'll end by describing how we have begun to test this framework using data from large-scale corpora of social media conversations.
Bio. Michael C. Frank is Associate Professor of Psychology at Stanford University. He earned his BS from Stanford University in Symbolic Systems in 2005 and his PhD from MIT in Brain and Cognitive Sciences in 2010. He studies both adults' language use and children's language learning and how both of these interact with social cognition. His work uses behavioral experiments, computational tools, and novel measurement methods including large-scale web-based studies, eye-tracking, and head-mounted cameras.
15:00-15:30 Coffee Break
15:30-17:00
Social Media Session
A Language Detection System for Short Chats in Mobile Games
Pidong Wang, Nikhil Bojja, Shivasankari Kannan
Long Nights, Raining Days, and Misspent Youth: Automatically Extracting and Categorizing Occasions Associated with Consumer Products
David Bracewell
A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination
Matthew Riemer, Sophia Krasikov, Harini Srinivasan
17:00-17:10 Best Paper Award/Closing

Paper Submission

SocialNLP @ WWW 2015 SocialNLP @ NAACL 2015
Page Limit 6 pages 8 pages
(allow additional 2 pages for references)
Paper Template ACM SIG Proceedings Template NAACL Pubs Template
Submission Site SocialNLP 2015 @ EasyChair SocialNLP 2015 @ START V2

SocialNLP review is double-blind. Therefore, please anonymize your submission: do not put the author(s) names or affiliation(s) at the start of the paper, and do not include funding or other acknowledgments in papers submitted for review.

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@WWW 2015, the workshop proceedings will be published in ACM Digital Library. For SocialNLP@NAACL 2015, the workshop proceedings will be published in ACL Anthology.

To pursue high quality submission, we will have a best paper award of SocialNLP 2015 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 Cognitive Computation (CogComp) and the International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP).

Registration

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.

Invited Speaker

TBA.

Important Dates

SocialNLP @ WWW 2015 SocialNLP @ NAACL 2015
Submission Deadline Jan 24, 2015
Extended to Feb 6, 2015
(23:59 Hawaii Standard Time)
Feb 25, 2015
Extended to Mar 7, 2015
(23:59 Hawaii Standard Time)
Author Notification Feb 22, 2015
Postponed to Feb 28, 2015
Mar 20, 2015
Camera-ready Submission Mar 06, 2015 Mar 30, 2015
Workshop Date May 19, 2015 Jun 05, 2015

Program Committee

  • Alexandra Balahur, European Commission¡¦s Joint Research Centre
  • Chia-Hui Chang, National Central University
  • Berlin Chen, National Taiwan Normal University
  • Hsin-Hsi Chen, National Taiwan University
  • Munmun De Choudhury, Georgia Institute of Technology
  • Min-Yuh Day, Tamkang University
  • Amitava Das, University of North Texas
  • Dipankar Das, Jadavpur University
  • Jennifer Foster, Dublin City University
  • June-Jei Kuo, National Chung Hsing University
  • Chuan-Jie Lin, National Taiwan Ocean University
  • Rafal Rzepka, Hokkaido University
  • Yohei Seki, University of Tsukuba
  • Daniela Stockmann, Leiden University
  • Keh-Yih Su, Academia Sinica
  • Ming-Feng Tsai, National ChengChi University
  • Chi Wang, Microsoft Research
  • Hsin-Min Wang, Academia Sinica
  • Jenq-Haur Wang, National Taipei University of Technology
  • Shih-Hung Wu, Chaoyang University of Technology
  • Yejun Wu, Louisiana State University
  • Yunqing Xia, Tsinghua University
  • Ruifeng Xu, Harbin Institute of Technology, Shenzhen Graduate School
  • Rui Yan, Baidu Inc.
  • Yi-Hsuan Yang, Academia Sinica
  • Kevin Zhang, Beijing Institute of Technology

Organizers

Past SocialNLP

Contacts

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] iis.sinica.edu.tw and ctli [AT] citi.sinica.edu.tw. Thanks!

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