Special Issue of the journal Computational Linguistics on:
Language in Social Media: Exploiting discourse and other contextual
*** Deadline 15th October 2017 (11:59 pm PST) ***
For more details see: http://www.sfu.ca/~mtaboada/
Farah Benamara — IRIT, Toulouse University (email@example.com )
Diana Inkpen — University of Ottawa (firstname.lastname@example.org )
Maite Taboada — Simon Fraser University (email@example.com )
**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
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.
**Topics of interest**
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.
• Lexical semantic resources, corpora and annotations of semantic and
pragmatic phenomena in social media.
• The role of extra-linguistic information in improving content-based
social media applications.
• Figurative language detection (metaphor, irony, sarcasm).
• Discourse processing and argumentation mining of social media texts.
• Pragmatic phenomena in computational social linguistics.
• Intention detection (e.g., intention to purchase a product, or vote for a
particular candidate, but also other behaviours such as suicide).
• Detection of offensive and abusive language.
• Fake news detection. Tracking rumours.
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:
• Social structure and position analysis using microblog content;
• Sentiment/opinion retrieval, extraction and classification
• Tracking and summarization of opinion
• Emotion detection.
**Paper format and reviewing policy**
Papers should be submitted according to the Computational Linguistics
Send papers using the online submission system:
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/