Call for papers for a Special Issue of the
journal Computational Linguistics
Deadline: October 15th, 2017
Social media content (SMC) is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects and past experiences. This content (ranging from blogs, fora, reviews, and various social networking sites) has specific characteristics that are often referred as the five V's: volume, variety, velocity, veracity, and value. Most of them are short online conversational posts or comments often accompanied by non-linguistic contextual information, including metadata such as the social network of each user and their interactions with other users. Exploiting the context of a word or a sentence increases the amount of information we can get from it and enables novel applications. Such rich contextual information, however, makes natural language processing (NLP) of SMC a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as such methods take into account neither the interactive dimension nor the particular nature of this data, which shares properties of both spoken and written language.
Most research on NLP for social media focuses primarily on content-based processing of the linguistic information, using lexical semantics (e.g., discovering new word senses or multiword expressions) or semantic analysis (opinion extraction, irony detection, event and topic detection, geo-location detection) (Londhe et al., 2016; Aiello et al., 2013; Inkpen et al., 2015; Ghosh et al., 2015). Other research explores the interactions between content and extra-linguistic or extra-textual features like time, place, author profiles, demographic information, conversation thread and network structure, showing that combining linguistic data with network and/or user context improves performance over a baseline that uses only textual information (West et al., 2014; Karoui et al., 2015; Volkova et al., 2014; Ren et al., 2016).
We expect that papers in this special issue will contribute to a deeper understanding of these interactions from a new perspective of discourse interpretation. We believe that we are entering a new age of mining social media data, one that extracts information not just from individual words, phrases and tags, but also uses information from discourse and the wider context. Most of the “big data” revolution in social media analysis has examined words in isolation, a “bag-of-words” approach. We believe it is possible to investigate big data, and social media data in general, by exploiting contextual information.
We encourage submission of papers that address deep issues in linguistics, computational linguistics and social science. In particular, our focus is on the exploitation of contextual information within the text (discourse, argumentation chains) and extra-linguistic information (social network, demographic information, geo-location) to improve NLP applications and help building pragmatic-based NLP systems. The special issue aims also to bring researchers that propose new solutions for processing SMC in various use-cases including sentiment analysis, detection of offensive content, and intention detection. These solutions need to be reliable enough in order to prove their effectiveness against shallow bag-of-words approaches or content-based approaches alone.
We are particularly interested in submissions that address the topics below, by leveraging the role of discourse and/or other contextual information. We believe there are novel and interesting approaches that can be developed over the next few years.
We also welcome contributions and comparisons on already studied topics like the following, but submissions need to highlight the role of discourse and/or other contextual phenomena:
Papers should be submitted according to the
Computational Linguistics style:http://cljournal.org/
Send papers using the online submission system: http://cljournal.org/submissions.html. In Step 1 of the submission process, please select 'Special Issue: Language in Social Media' under the 'Journal Section' heading.
Please note that papers submitted to a special issue undergo the same reviewing process as regular papers. Special issues are the same length as regular issues (at most 5-6 papers) http://cljournal.org/specialissues.html.
Paper submission deadline: October 15, 2017 (11:59 pm PST)
socialmedia.coli AT gmail.com
Aiello, L. M., Petkos, G., Martn, C. J., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., and Jaimes, A. (2013). Sensing Trending Topics in Twitter. IEEE Trans. Multimedia, 15(6), 1268-1282.
Benamara, F., Taboada, M., and Mathieu, Y. (2017). Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications. Computational Linguistics 43(1): 201-264. .
Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J. A., and Reyes, A. (2015). Semeval-2015 task 11: Sentiment Analysis of Figurative Language in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2015, pages 470-478.
Inkpen, D., Liu, J., Farzindar, A., Kazemi, F., and Ghazi, D. (2015). Detecting and Disambiguating Locations Mentioned in Twitter messages. In Computational Linguistics and Intelligent Text Processing, CICLing, pages 321-332.
Karoui, J., Benamara, F., Moriceau, V., Aussenac-Gilles, N., and Belguith, L. H. (2015). Towards a Contextual Pragmatic Model to Detect Irony in Tweets. In Proceedings of the 53rd ACL-IJCNLP Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL, pages 644-650.
Londhe, N., Srihari, R. K., and Gopalakrishnan, V. (2016). Time-Independent and Language-Independent Extraction of Multiword Expressions from Twitter. In 26th International Conference on Computational Linguistics, COLING, pages 2269-2278.
Ren, Y., Zhang, Y., Zhang, M., and Ji, D. (2016). Context-Sensitive Twitter Sentiment Classification Using Neural Network. In Proceedings of the Thirtieth Conference on Artificial Intelligence, AAAI, pages 215-221.
Volkova, S., Coppersmith, G., and Durme, B. V. (2014). Inferring User Political Preferences from Streaming Communications. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, pages 186-196.
West, R., Paskov, H. S., Leskovec, J., and Potts, C. (2014). Exploiting Social Network Structure for Person-to-Person Sentiment Analysis. TACL, 2, 297-310.