Dr. Maite Taboada - Special Issue of Computational Linguistics
Dr. Maite Taboada, Professor in the Department of Linguistics, is editing a Special Issue of the journal Computational Linguistics, “Language in Social Media: Exploiting discourse and other contextual information.”
From the call for papers:
“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).”
This Special Issue will feature papers that “will contribute to a deeper understanding of these interactions from a new perspective of discourse interpretation” and “address deep issues in linguistics, computational linguistics and social science.”
Visit Dr. Taboada’s webpage for more information on topics of interest and submission guidelines. The deadline to submit papers is October 15, 2017.
Contact socialmedia.coli AT gmail.com for more information.