We are pleased to welcome two visiting professors to our lab. María de los Ángeles Gómez-González from the University of Santiago de Compostela will be visiting July-December 2018. Cliff Goddard from Griffith University will be at SFU October-November 2018. Welcome!
Posts and Events
We analyzed more than 1.5 million comments from 3 news organizations. We found more constructive comments than we expected, that toxicity happens equally across topics and that some news outlets have better commenters than others. The full story:
- Gautam, V. and M. Taboada (2019) Constructiveness and Toxicity in Online News Comments. Report. Simon Fraser University. November 2019.
We also published a short version as an op-ed for The Tyee:
- Gautam, V. and M. Taboada. Hey Tyee Commenters! Scholars Studied You. Here's What They Found. The Tyee (online). November 6, 2019.
SOCC, the SFU Opinion and Comments Corpus, has been available online for a while. Now the paper describing it is also online:
- Kolhatkar, V.,H. Wu, L. Cavasso, E. Francis, K. Shukla and M. Taboada (to appear) The SFU Opinion and Comments Corpus: A corpus for the analysis of online news comments. Corpus Pragmatics.
Maite is participating in a project that studies online abuse against candidates in the 2019 Canadian federal election, with Heidi Tworek from the University of British Columbia as principal investigator:
Paper on data quality for misinformation detection now available, open access:
Asr, F.T. and M. Taboada (2019) Big data and quality data for fake news and misinformation detection. Big Data & Society January-June 2019:1-14.
Paper too long to read? There's a video abstract!
We launched the Gender Gap Tracker in Ottawa!
Maite attended this fabulous event with Maryam Monsef, Minister for Women and Gender Equality and Dr. Joy Johnson, SFU's VP Research, hosted by Shari Graydon of Informed Opinions.
The Gender Gap Tracker, developed within our lab with Informed Opinions, monitors the proportion of women and men quoted in news stories in mainstream Canadian media (in English).
We are having great media coverage for this event. You can check the Conversation piece for an explanation of the tracker, and see other media mentions:
- Tracking the gender gap in Canadian media (The Conversation)
- Online tool gives media outlets incentive to achieve gender parity (The Toronto Star)
- SFU partners with Informed Opinions to create Gender Gap Tracker (SFU News)
- In numbers there is strength (Ottawa Citizen and Montreal Gazette)
A short video about Maite's research, especially on sentiment analysis.
July 31, 2018
June 22, 2018
The Discourse Lab is hosting a talk by visiting researcher Maite Martín.
Title: Affective and Social Computing in Spanish using Human Language Technologies
Speaker: Maite Martín, Universidad de Jaén (Spain)
When: Friday, June 22, 1 pm
Where: RCB 7402
Abstract: In this talk I will present some past projects and work in progress in which my research group SINAI (Sistemas INteligentes de Acceso a la Información – Intelligent systems for information access) is involved. Our area of specialization focuses on the development of techniques and tools to solve problems related to Human Language Technologies (HLT). I will briefly discuss our research oriented to Information Retrieval Systems (IRS) mainly in the biomedical domain. We are integrating heterogenous sources of medical and general information (UMLS, Google, SciELO, Dbpedia…) in order to improve the final IRS. I will also highlight the work we have done in the field of affective computing, mainly focused on Spanish and on the social web. Although lot of work has been already done in opinion mining, we think the real challenge is to recognize and analyse emotion expressed in textual documents. Finally, I describe future projects related to early detection of mental health problems (depression, anxiety, cyberbullying…) by analysing the textual information written in social networks. I will show some demos implemented by SINAI.
Dr. Maite Martín is Associate Professor in the Computer Science department of the University of Jaén (Spain). She received her Master's degree in Computer Science at the University of Granada, and her PhD in Computer Science at the University of Málaga. She has been teaching different courses at the University since 1995. She has been a member of the research group SINAI (Sistemas INteligentes de Acceso a la Información – Intelligent systems for information access) since 2000. Her scientific interests include several areas related to Human Language Technologies such as Information Retrieval, Machine Learning, Text Mining and Sentiment Analysis. She has been a member of programme committees of several international and national conferences. In addition, she has participated in more than 30 national research projects serving as lead researcher in some of them. She has published more than a hundred conference papers, journal papers, books and book chapters. Martín is the current treasurer of the Spanish Society of Natural Language Processing (SEPLN – Sociedad Española para el Procesamiento del Lenguaje Natural). She is editor of a number of issues of the journal Procesamiento de Lenguaje Natural (Natural Language Processing). She has also been an invited speaker at several conferences.
We participated in the BCTech Summit! We did demos of our two systems, content moderation and fake news detection. You can try them too!
Article in The Conversation about our research on online news comments. Trolls, toxicity and construtive conversations.
Maite is part of a panel discussing the documentary The Cleaners, about content moderation in social media.
SFU News story on our research.
Jon Alkorta is a visiting PhD researcher from the University of the Basque Country in Spain. He will be in the lab between February and May, doing research on rhetorical relations and sentiment in Basque.
We have just released the SFU Opinion and Comments Corpus (SOCC), a corpus for the analysis of online news comments. Our corpus contains comments and the articles from which the comments originated. The articles are all opinion articles, not hard news articles. The corpus is larger than any other currently available comments corpora, and has been collected with attention to preserving reply structures and other metadata. In addition to the raw corpus, we also present annotations for four different phenomena: constructiveness, toxicity, negation and its scope, and appraisal.
Full details, and download link, are available from our GitHub project page: https://github.com/sfu-discourse-lab/SOCC
For more information about this work, please see our papers.
- Kolhatkar, V., H. Wu, L. Cavasso, E. Francis, K. Shukla and M. Taboada (2018) The SFU Opinion and Comments Corpus: A corpus for the analysis of online news comments. Journal paper under review.
- Kolhatkar. V. and M. Taboada (2017) Using New York Times Picks to identify constructive comments. Proceedings of the Workshop Natural Language Processing Meets Journalism, Conference on Empirical Methods in Natural Language Processing. Copenhagen. September 2017.
- Kolhatkar, V. and M. Taboada (2017) Constructive language in news comments. Proceedings of the 1st Abusive Language Online Workshop, 55th Annual Meeting of the Association for Computational Linguistics. Vancouver. August 2017, pp. 11-17.
November 15, 2017
Our postdoctoral researcher, Katharina Ehret, has been featured in an article on the Faculty of Arts and Social Sciences webpage.
October 18, 2017
Dr. Cliff Goddard from Griffith University in Australia is visiting the Discourse Lab between October 18 and November 10. Dr. Goddard is a long-time collaborator, and is here thanks to an SFU-Griffith Collaborative Travel Grant.
The lab has grown! We have two new undergraduate students, a new master's student, and two new postdocs. It'll be a busy semester!
June 30, 2017
Speaker: Muhammad Abdul-Mageed, Assistant Professor of Information Science in the iSchool at UBC.
Abstract: Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutchik’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.
Sonya Chik will be a visiting PhD researcher in the lab until August. She is conducting cross-linguistic research on socio-semiotic processes in privacy policies, using a systemic-functional lingusitics approach.
February 24, 2017
Presentation on Spark:
-- Spark dataframe udf
-- search engine, Spark GraphFrame
-- Spark MLLIB, Scikit Learn
-- Spark pipeline with coreNLP
Installation instructions for WebAnno
January 20, 2017
Speaker: Enamul Hoque
Abstract: Analyzing and gaining insights from a large amount of online conversations can be quite challenging for a user, especially when the discussions become very long. During my doctoral research, I have focused on integrating Information Visualization (InfoVis) with Natural Language Processing (NLP) techniques to better support the user’s task of exploring and analyzing conversations. For this purpose, I have designed a visual text analytics system that supports the user exploration, starting from a possibly large set of conversations, then narrowing down to a subset of conversations, and eventually drilling-down to a set of comments of one conversation. Our evaluations through case studies with domain experts and a formal user study with regular blog readers illustrate the potential benefits of our approach, when compared to a traditional blog reading interface.