Post Doctoral Position (12 months), Natural Language Processing: ?Online hate speech against migrants?

Keywords: hate speech, migrants, social media, natural language processing.

Supervisors : Irina Illina and Dominique Fohr. The applicant will also collaborate with CREM Laboratory

Start: end of 2018 ? begin of 2019

Location: INRIA-Loria, Nancy, France

Duration: 1 year

To apply:  send the following documents to illina@loria.fr and  dominique.fohr@loria.fr  as soon as possible and no later than September 25th, 2018:

• CV

• motivation letter

• PhD thesis if already completed, or a description of the work in progress otherwise

• a copy of your publications

• • a recommendation letter from the supervisor of your PhD thesis, and up to two other recommendation letters.

  The ideal applicant should have:

  • A PhD in NLP

  • A solid background in statistical machine learning.

  • Strong publications.

  • Solid programming skills to conduct experiments.

  • Good level in English.

     

     

    Context:

    According to the 2017 International Migration Report, the number of international migrants worldwide has continued to grow rapidly in recent years, reaching 258 million in 2017, up from 220 million in 2010 and 173 million in 2000. In 2017, 64 per cent of all international migrants worldwide ? equal to 165 million international migrants ? lived in high-income countries; 78 million of them were residing in Europe. A key reason for the difficulty of EU leaders to take a decisive and coherent approach to the refugee crisis has been the high levels of public anxiety about immigration and asylum across Europe. Indeed, across the EU, attitudes towards asylum and immigration have hardened in recent years because of: (i) the increase in the number and visibility of migrants in recent years, (ii) the economic crisis and austerity policies enacted since the 2008 Global Financial Crisis, (iii) the role of the mass media in influencing public and elite political attitudes towards asylum and migration. Refugees and migrants tend to be framed negatively as a problem, potentially nourishing.

    The BRICkS ? Building Respect on the Internet by Combating Hate Speech ?  EU project has revealed a significant increase of the use of hate speech towards immigrants and minorities, which are often blamed to be the cause of current economic and social problems. The participatory web and the social media seem to accelerate this tendency, accentuated by the online rapid spread of fake news which often corroborate online violence towards migrants.

    More and more audio/video/text appear on Internet each day. About 300 hours of multimedia are uploaded per minute. In these multimedia sources, manual content retrieval is difficult or impossible. The classical approach for spoken content retrieval from multimedia documents is an automatic text retrieval. Automatic text classification is one of the widely used technologies for the above purposes. In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. We will use these methodologies to perform one of the important tasks of text classification: automatic hate speech detection.

    Our methodology in the hate speech classification will be related on the recent approaches for text classification with neural networks and word embeddings. In this context, fully connected feed forward networks (Iyyer et al., 2015; Nam et al., 2014), Convolutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015) and also Recurrent/Recursive Neural Networks (RNN) (Dong et al., 2014) have been applied.

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    Objectives:

    Within this context and problematic, the post-doc position aims to analyze hate speech towards migrants in social media and more particularly on Twitter. This post-doc position aims at proposing concepts and software components (Hate Speech Domain Specific Analysis and related software tools in connection with migrants in social media) to bridge the gap between conceptual requirements and multi-source information from social media. Automatic hate speech detection software will be experimented in the modeling of various hate speech phenomenon and assess their domain relevance. 

    The language of the analysed messages will be primarily French, although links with other languages (including messages written in English) may appear throughout the analysis.

  • • References

      Dai, A. M. and Le, Q. V. (2015). ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc

      Delgado R., Stefancic J. (2014), ?Hate speech in cyberspace?, Wake Forest Law Review, 49.

      Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., and Xu, K. (2014). ?Adaptive recursive neural network for target-dependent twitter sentiment classification?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL, Baltimore, MD, USA, Volume 2: pages 49-54.

      Johnson, R. and Zhang, T. (2015). ?Effective use of word order for text categorization with convolutional neural networks?. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103-112.

      Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé, H. (2015). ?Deep unordered composition rivals syntactic methods for text classification?. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 1681-1691.

      Kim, Y. (2014). ?Convolutional neural networks for sentence classification?. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751.

      King R. D., Sutton G. M. (2013). High times for hate crimes: Explaining the temporal clustering of hate-motivated offending. Criminology, 51 (4), 871?894.

      Mikolov, T., Yih, W.-t., and Zweig, G. (2013a). ?Linguistic regularities in continuous space word representations?. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746-751.

      Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc.

      Nam, J., Kim, J., Loza Menc__a, E., Gurevych, I., and Furnkranz, J. (2014). ?Large-scale multi-label text classification ? revisiting neural networks?. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-14), Part 2, volume 8725, pages 437-452.

      Schieb C, Preuss M (2016), Governing Hate Speech by Means of Counter Speech on Facebook, 66th ICA Annual Conference, Fukuoka, Japan.