A summary of our contributions on the Gender Gap Tracker:
1. Dashboards and code:
2. Op-eds and commentary:
3. Academic papers
Op-ed on the Gender Gap Tracker and its third birthday:
And a more expanded blog post with more details, publications, and statistics:
Op-ed on the language of fake news, in Items, the journal of the Social Sciences Research Council:
An update on our project about online news comments. Three more papers (#8, #9, and #10 below) on news comments and a summary of our findings:
- Raw data
- Paper describing the raw data (with small annotations)
- Annotated data (12,000 comments), in collaboration with Jigsaw
- Kolhatkar, V., Thain, N., Sorensen, J., Dixon, L., Taboada, M., 2020. C3: The Constructive Comments Corpus. Jigsaw and Simon Fraser University. Dataset. DOI: 10.25314/ea49062a-5cf6-4403-9918-539e15fd7b52.
- Paper describing the large-scale annotation
- Register analysis: Are news comments like conversations? (tl;dr: NO)
- Subjectivity analysis: How complex are news comments vs. opinion articles? (tl;dr: it's complex)
- Constructiveness and toxicity across 3 newspapers:
- NEW!!! Register analysis, again. If not like conversation, what are comments like? (Answer: a hybrid register):
- NEW!!! Appraisal analysis. Comments are very negative. They tend to express evaluation as Judgement or Appreciation (rather than Affect).
- NEW!!! Concessive relations in comments. Concessions have an interpersonal function and are used for evaluation and argumentation, especially in constructive comments.
We have learned a lot about online news comments. Mostly, that they are very complex and more like essays than casual conversation.
We have been working for almost 3 years now on a project analyzing the gender gap in Canadian media. We have created a summary dashboard with overall statistics and a research dashboard analyzing topics and top-quoted sources. We can also now share the great news that a research paper on the Gender Gap Tracker has been published!
Our findings:
- In 2 years of Canadian news media, the percentage of women quoted is regularly below 30%
- Women authors quote more women
- Politicians dominate in the news
- NLP can help us find these patterns in data
Op-ed on women quoted during COVID-19:
Op-ed by Lucas Chambers and Maite Taboada on media coverage of elections:
The lab has been busy analyzing news comments. Here, all in one place, are the papers and the data that we have produced:
- Raw data
- Paper describing the raw data (with small annotations)
- Annotated data
- Kolhatkar, V., Thain, N., Sorensen, J., Dixon, L., Taboada, M., 2020. C3: The Constructive Comments Corpus. Jigsaw and Simon Fraser University. Dataset. DOI: 10.25314/ea49062a-5cf6-4403-9918-539e15fd7b52.
- Paper describing the large-scale annotation
- Register analysis: Are news comments like conversations? (tl;dr: NO)
- Subjectivity analysis: How complex are news comments vs. opinion articles? (tl;dr: it's complex)
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:
Preliminary results of the analysis will be made available early in 2020.
A short video about Maite's research, especially on sentiment analysis.
The Discourse Lab is hosting a talk by visiting researcher Maite MartĂn.
Title: Affective and Social Computing in Spanish using Human Language Technologies
Speaker: Maite MartĂn, Universidad de JaĂ©n (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 InformaciĂłn â 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, DbpediaâŠ) 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, cyberbullyingâŠ) by analysing the textual information written in social networks. I will show some demos implemented by SINAI.
Short Bio
Dr. Maite MartĂn is Associate Professor in the Computer Science department of the University of JaĂ©n (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 MĂĄlaga. 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 InformaciĂłn â 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. MartĂn is the current treasurer of the Spanish Society of Natural Language Processing (SEPLN â 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.
A couple of interviews on trolls and social media:
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.
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.
Contact:
Varada Kolhatkar (vkolhatk@sfu.ca)
Maite Taboada (mtaboada@sfu.ca)
Visiting Researcher
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!
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 Plutchikâs 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.
Presentation on Spark:
-- MapReduce
-- Spark dataframe udf
-- search engine, Spark GraphFrame
-- Spark MLLIB, Scikit Learn
-- Spark pipeline with coreNLP
Installation instructions for WebAnno
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 userâs 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.