Svetlana Kiritchenko


2022

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Extracting Age-Related Stereotypes from Social Media Texts
Kathleen C. Fraser | Svetlana Kiritchenko | Isar Nejadgholi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community. Here, we present a method for extracting age-related stereotypes from Twitter data, generating a corpus of 300,000 over-generalizations about four contemporary generations (baby boomers, generation X, millennials, and generation Z), as well as “old” and “young” people more generally. By employing word-association metrics, semi-supervised topic modelling, and density-based clustering, we uncover many common stereotypes as reported in the media and in the psychological literature, as well as some more novel findings. We also observe trends consistent with the existing literature, namely that definitions of “young” and “old” age appear to be context-dependent, stereotypes for different generations vary across different topics (e.g., work versus family life), and some age-based stereotypes are distinct from generational stereotypes. The method easily extends to other social group labels, and therefore can be used in future work to study stereotypes of different social categories. By better understanding how stereotypes are formed and spread, and by tracking emerging stereotypes, we hope to eventually develop mitigating measures against such biased statements.

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Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
Kathleen C. Fraser | Svetlana Kiritchenko | Esma Balkir
Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)

In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong. This is typically done in a bottom-up fashion, by exposing the model to different scenarios, annotated with human moral judgements. One question, however, is whether the trained models actually learn any consistent, higher-level ethical principles from these datasets – and if so, what? Here, we probe the Allen AI Delphi model with a set of standardized morality questionnaires, and find that, despite some inconsistencies, Delphi tends to mirror the moral principles associated with the demographic groups involved in the annotation process. We question whether this is desirable and discuss how we might move forward with this knowledge.

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Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
Esma Balkir | Svetlana Kiritchenko | Isar Nejadgholi | Kathleen Fraser
Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)

Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fairer. However, exactly how an XAI method can help in combating biases is often left unspecified. In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.

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Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
Esma Balkir | Isar Nejadgholi | Kathleen Fraser | Svetlana Kiritchenko
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection. Although feature attribution models usually provide a single importance score for each token, we instead provide two complementary and theoretically-grounded scores – necessity and sufficiency – resulting in more informative explanations. We propose a transparent method that calculates these values by generating explicit perturbations of the input text, allowing the importance scores themselves to be explainable. We employ our method to explain the predictions of different hate speech detection models on the same set of curated examples from a test suite, and show that different values of necessity and sufficiency for identity terms correspond to different kinds of false positive errors, exposing sources of classifier bias against marginalized groups.

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Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors
Isar Nejadgholi | Kathleen Fraser | Svetlana Kiritchenko
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of abusive language continually emerge in online discussions in response to current events (e.g., COVID-19), and the deployed abuse detection systems should be updated regularly to remain accurate. In this paper, we show that general abusive language classifiers tend to be fairly reliable in detecting out-of-domain explicitly abusive utterances but fail to detect new types of more subtle, implicit abuse. Next, we propose an interpretability technique, based on the Testing Concept Activation Vector (TCAV) method from computer vision, to quantify the sensitivity of a trained model to the human-defined concepts of explicit and implicit abusive language, and use that to explain the generalizability of the model on new data, in this case, COVID-related anti-Asian hate speech. Extending this technique, we introduce a novel metric, Degree of Explicitness, for a single instance and show that the new metric is beneficial in suggesting out-of-domain unlabeled examples to effectively enrich the training data with informative, implicitly abusive texts.

2021

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Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model
Kathleen C. Fraser | Isar Nejadgholi | Svetlana Kiritchenko
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.

2020

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SOLO: A Corpus of Tweets for Examining the State of Being Alone
Svetlana Kiritchenko | Will Hipson | Robert Coplan | Saif M. Mohammad
Proceedings of the Twelfth Language Resources and Evaluation Conference

The state of being alone can have a substantial impact on our lives, though experiences with time alone diverge significantly among individuals. Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept of loneliness, a negative state of dissatisfaction with the quality of one’s social interactions. Here, for the first time, we conduct a large-scale computational analysis to explore how the terms associated with the state of being alone are used in online language. We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms solitude, lonely, and loneliness. We use SOLO to analyze the language and emotions associated with the state of being alone. We show that the term solitude tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms lonely and loneliness frequently co-occur with negative, low-dominance words (e.g., scared, depressed), which confirms the conceptual distinctions made in psychology. We also show that women are more likely to report on negative feelings of being lonely as compared to men, and there are more teenagers among the tweeters that use the word lonely than among the tweeters that use the word solitude.

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On Cross-Dataset Generalization in Automatic Detection of Online Abuse
Isar Nejadgholi | Svetlana Kiritchenko
Proceedings of the Fourth Workshop on Online Abuse and Harms

NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions. Also, the ambiguity in class definitions inherited in this task aggravates the discrepancies between source and target datasets. We explore the topic bias and the task formulation bias in cross-dataset generalization. We show that the benign examples in the Wikipedia Detox dataset are biased towards platform-specific topics. We identify these examples using unsupervised topic modeling and manual inspection of topics’ keywords. Removing these topics increases cross-dataset generalization, without reducing in-domain classification performance. For a robust dataset design, we suggest applying inexpensive unsupervised methods to inspect the collected data and downsize the non-generalizable content before manually annotating for class labels.

2019

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Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition
Shima Asaadi | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Bigrams (two-word sequences) hold a special place in semantic composition research since they are the smallest unit formed by composing words. A semantic relatedness dataset that includes bigrams will thus be useful in the development of automatic methods of semantic composition. However, existing relatedness datasets only include pairs of unigrams (single words). Further, existing datasets were created using rating scales and thus suffer from limitations such as in consistent annotations and scale region bias. In this paper, we describe how we created a large, fine-grained, bigram relatedness dataset (BiRD), using a comparative annotation technique called Best–Worst Scaling. Each of BiRD’s 3,345 English term pairs involves at least one bigram. We show that the relatedness scores obtained are highly reliable (split-half reliability r= 0.937). We analyze the data to obtain insights into bigram semantic relatedness. Finally, we present benchmark experiments on using the relatedness dataset as a testbed to evaluate simple unsupervised measures of semantic composition. BiRD is made freely available to foster further research on how meaning can be represented and how meaning can be composed.

2018

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SemEval-2018 Task 1: Affect in Tweets
Saif Mohammad | Felipe Bravo-Marquez | Mohammad Salameh | Svetlana Kiritchenko
Proceedings of the 12th International Workshop on Semantic Evaluation

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.

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DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning
Habibeh Naderi | Behrouz Haji Soleimani | Saif Mohammad | Svetlana Kiritchenko | Stan Matwin
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features of a tweet as well as the emotion information conveyed by each word in it. We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features. As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words. An SVR regressor is then trained over the full set of features. We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72% on the task of tweet emotion intensity prediction.

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Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Svetlana Kiritchenko | Saif Mohammad
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 ‘Affect in Tweets’. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available.

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Agree or Disagree: Predicting Judgments on Nuanced Assertions
Michael Wojatzki | Torsten Zesch | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.

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Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories
Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Quantifying Qualitative Data for Understanding Controversial Issues
Michael Wojatzki | Saif Mohammad | Torsten Zesch | Svetlana Kiritchenko
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Svetlana Kiritchenko | Saif Mohammad
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best–worst scaling (BWS) is an alternative method of annotation that is claimed to produce high-quality annotations while keeping the required number of annotations similar to that of rating scales. However, the veracity of this claim has never been systematically established. Here for the first time, we set up an experiment that directly compares the rating scale method with BWS. We show that with the same total number of annotations, BWS produces significantly more reliable results than the rating scale.

2016

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Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling
Svetlana Kiritchenko | Saif M. Mohammad
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Sentiment Composition of Words with Opposing Polarities
Svetlana Kiritchenko | Saif M. Mohammad
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Sentiment Lexicons for Arabic Social Media
Saif Mohammad | Mohammad Salameh | Svetlana Kiritchenko
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Existing Arabic sentiment lexicons have low coverage―with only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We compare the usefulness of new and old sentiment lexicons in the downstream application of sentence-level sentiment analysis. Our baseline sentiment analysis system uses numerous surface form features. Nonetheless, the system benefits from using additional features drawn from sentiment lexicons. The best result is obtained using the automatically generated Dialectal Hashtag Lexicon and the Arabic translations of the NRC Emotion Lexicon (accuracy of 66.6%). Finally, we describe a qualitative study of the automatic translations of English sentiment lexicons into Arabic, which shows that about 88% of the automatically translated entries are valid for English as well. Close to 10% of the invalid entries are caused by gross mistranslations, close to 40% by translations into a related word, and about 50% by differences in how the word is used in Arabic.

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Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases
Svetlana Kiritchenko | Saif Mohammad
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Sentiment composition is the determining of sentiment of a multi-word linguistic unit, such as a phrase or a sentence, based on its constituents. We focus on sentiment composition in phrases formed by at least one positive and at least one negative word ― phrases like ‘happy accident’ and ‘best winter break’. We refer to such phrases as opposing polarity phrases. We manually annotate a collection of opposing polarity phrases and their constituent single words with real-valued sentiment intensity scores using a method known as Best―Worst Scaling. We show that the obtained annotations are consistent. We explore the entries in the lexicon for linguistic regularities that govern sentiment composition in opposing polarity phrases. Finally, we list the current and possible future applications of the lexicon.

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A Dataset for Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We can often detect from a person’s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.

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The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Svetlana Kiritchenko | Saif Mohammad
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko | Saif Mohammad | Mohammad Salameh
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Parinaz Sobhani | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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SemEval-2015 Task 10: Sentiment Analysis in Twitter
Sara Rosenthal | Preslav Nakov | Svetlana Kiritchenko | Saif Mohammad | Alan Ritter | Veselin Stoyanov
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Sentiment after Translation: A Case-Study on Arabic Social Media Posts
Mohammad Salameh | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
Svetlana Kiritchenko | Xiaodan Zhu | Colin Cherry | Saif Mohammad
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets
Xiaodan Zhu | Svetlana Kiritchenko | Saif Mohammad
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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An Empirical Study on the Effect of Negation Words on Sentiment
Xiaodan Zhu | Hongyu Guo | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
Saif Mohammad | Svetlana Kiritchenko | Xiaodan Zhu
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2011

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Lexically-Triggered Hidden Markov Models for Clinical Document Coding
Svetlana Kiritchenko | Colin Cherry
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies