[Rstlist] Call for Papers: Special Issue of Computational Linguistics on Language in Social Media
Maite Taboada
mtaboada at sfu.ca
Thu Apr 27 16:39:14 UTC 2017
Call for Papers: Special Issue of the journal Computational
Linguisticson Language in Social Media
** Apologies for cross-posting **
========================================
Special Issue of the journal Computational Linguistics on:
Language in Social Media: Exploiting discourse and other contextual
information
*** Deadline 15th October 2017 (11:59 pm PST) ***
For more details see: http://www.sfu.ca/~mtaboada/coli-si.html
========================================
**Guest editors**
Farah Benamara - IRIT, Toulouse University (benamara at irit.fr)
Diana Inkpen - University of Ottawa (diana.inkpen at uottawa.ca)
Maite Taboada - Simon Fraser University (mtaboada at sfu.ca)
**Contact**
socialmedia.coli AT gmail.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 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.
**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
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.
**Deadline**
Paper submission deadline: October 15, 2017 (11:59 pm PST)
**References**
See http://www.sfu.ca/~mtaboada/coli-si.html
<http://www.sfu.ca/%7Emtaboada/coli-si.html>
--
Maite Taboada
Professor
Department of Linguistics
Simon Fraser University
8888 University Dr.
Burnaby, BC, V5A 1S6, Canada
Tel +1-778-782-5585
mtaboada at sfu.ca
http://www.sfu.ca/~mtaboada
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