[Corpora-List] Last CfP CSR NLP I 2022, DDL Extended to 24 April 2022

WAN, Mingyu [CBS] mingyu.wan at polyu.edu.hk
Thu Apr 7 15:28:53 CEST 2022

**apologies for cross-posting**

CSR NLP I @ LREC 2022 – The First Computing Social Responsibility Workshop -NLP Approaches to Corporate Social Responsibilities – Last Call for Papers Website: https://csr-nlp.github.io/CSR-NLP-2022/

Submission Deadline Extended to: 24 April, 2022

Submissions are now available through the START<https://www.softconf.com/lrec2022/CSR-NLP1/> site.

We call for full papers from 4 pages to 8 pages (plus more pages for references if needed), which must strictly follow the LREC stylesheet<https://lrec2022.lrec-conf.org/en/submission2022/authors-kit/>.

CSR-NLP I 2022 is a full-day workshop that aims to provide a venue for computational linguistic research and NLP methods on Corporate Social Responsibility (CSR) indexing/prediction/modelling. It bridges data resources, language theories, and NLP technologies on CSR and facilitates the detection of corporate images for analysing crucial financial issues of a wide interest, such as stock returns, equity cost, capital expenditure, and so on.

Location: co-located with LREC 2022<https://lrec2022.lrec-conf.org/en/>

Country: France

City: Palais du Pharo, Marseille

Workshop Date: Saturday 25 June, 2022

LREC 2022 and all its workshops will now be held entirely online to avoid the need for international travel and risk of spread of COVID-19.

Contact: Mingyu Wan, Bo Peng, Emmanuele Chersoni, Cindy Sing Bik Ngai

Contact email: mingyu.wan at polyu.edu.hk<mailto:mingyu.wan at polyu.edu.hk>


Workshop Description

Corporate Social Responsibility (CSR) as a shared grand challenge in business studies and in computational linguistics has not been tackled yet in the recently thriving financial NLP studies. These work so far have been more driven by the NLP downstream technology instead of the theoretical or real-world issues driving studies of economics or business. Conventional methods usually focus on shared values of companies such as sustainability, carbon footprint, diversity and inclusion, fair-trade, social justice, environmental impact. However, different businesses may breed additional and more specific areas of issues to address, such as pollution/emission, pharmacovigilance, food safety etc. Since some researchers in the NLP community have started to work on this topic, we propose to call for concerted efforts in this workshop to build up language resources, and to tackle some of the key issues, such as CSR ranking, CSR crisis, and CSR perception with corporate performance, etc. based on language technologies.

The ultimate goal of the current proposal is to identify and develop a niche research methodology that is highly competitive and world leading for CSR modelling. Tackling the Grand Challenges of the world by promoting mutual understanding through language and CSR is a necessary step towards tackling other grand challenges that also may be aided by language big data, deep neural networks, linguistic tools and methods, towards some of the trending issues in CSR studies such as environmental degradation and the climate crisis.

We can view the challenge of understanding and evaluating CSR as an NLP task learning CSR information from language big data, and to develop on annotated language resources. Thus, we propose to add the followings as potential areas of submissions for contributing to dataset/resource constructions and CSR prediction.

Scope and Topics

Topics of interest include, but not limited to:

* Data mining methods to collect and construct CSR/ESG data

* NLP methods to automatic evaluation and ranking of CSR

* Resources and evaluation strategies for NLP-CSR

* Extraction, annotation and integration of potential source data including corporate reports (esp. CSR reports), (environmental) impact studies, consumer complaints, records of donation related to CSR issues, records of violation and punishment related to CSR issues, news, and social media

* NLP technologies for tracking and evaluation of different aspects of societal and environmental concerns vis-a-via CSR, including (but not limited to), sustainability, carbon footprint, diversity and inclusion, fair trade, social justice, environmental impact, etc.

* Automatic detection and prevention of risks both as CSR obligations and risk management, including (but not limited to), pharmacovigilance, food safety, inequality and exclusion, workplace bullying, fake or inaccurate content, ethnic stereotyping and other insensitive languages

* Incorporating linguistic and behavioural theories and NLP technologies to interpret and model CSR positioning and perception

* Modelling the correlations between a company’s CSR policies, actions, and statements with the public evaluation of its CSR performance, and between public evaluation of CSR of a company and its market performance and brand value estimation

* Adaptation and synergizing NLP tasks and solutions such as Emotion and Sentiment Analysis, Event Detection, Fake News Detection, Information Quality, Irony and Sarcasm Detection, Metaphor Classification, Named Entity Recognition, etc.

--- Dr. Mingyu Wan (on behalf of the PC members of CSR NLP I)



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