--- title: "Aida Mostafazadeh Davani — author profile and papers" description: "Aida Mostafazadeh Davani, Google: 4 papers on Elamur Research (2020–2026), h-index 17 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/aida-mostafazadeh-davani/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/aida-mostafazadeh-davani/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Aida Mostafazadeh Davani # Aida Mostafazadeh Davani Aida Mostafazadeh Davani works on natural language processing for tasks where human judgement is subjective, such as hate speech detection, emotion and sentiment classification, and the detection of moral sentiment. A recurring question in the papers here is what gets lost when the differing labels of several annotators are collapsed into a single answer, and how classifiers absorb social bias from the data they learn from. The methods include building annotated corpora, designing multi-task neural models, applying post-hoc explanation techniques, and analysing annotator agreement across socio-demographic groups. The most cited work, [Dealing with Disagreements](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/), argues against merging labels before training. It proposes multi-annotator models in which each annotator's judgements are predicted by a separate output head built on a shared representation, and evaluates them on hate speech and emotion tasks. [Contextualizing Hate Speech Classifiers with Post-hoc Explanation](https://md.research.elamur.ai/free/en/papers/kennedy-2020-contextualizing-hate-speech-classifiers-post/) shows that classifiers lean heavily on group identifiers as signals of hate. It then adds a training penalty based on explanation scores, which pushes the models to attend to the surrounding context instead. [On Releasing Annotator-Level Labels and Information in Datasets](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/) compares how closely individual annotators match the majority label and asks whether that match differs across demographic groups. The newest paper, [The Moral Foundations Reddit Corpus](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/), turns to moral sentiment. It offers a new collection of Reddit comments, each coded by several trained annotators. The coding follows the revised Moral Foundations Theory, which separates fairness into equality and proportionality. The corpus extends an earlier Twitter-only resource to a different platform and to the updated set of categories. Davani is currently at [Google](https://md.research.elamur.ai/free/en/companies/google/), and earlier papers list the [University of Southern California](https://md.research.elamur.ai/free/en/universities/university-of-southern-california/). Frequent co-authors include [Vinodkumar Prabhakaran](https://md.research.elamur.ai/free/en/authors/vinodkumar-prabhakaran/), [Brendan Kennedy](https://md.research.elamur.ai/free/en/authors/brendan-kennedy/) and Mark Díaz. The papers here fall under [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) and [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/). Aida Mostafazadeh Davani has 4 papers in our library, in 2020–2026, most of them on [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Brendan Kennedy](https://md.research.elamur.ai/free/en/authors/brendan-kennedy/), [Mark Díaz](https://md.research.elamur.ai/free/en/authors/mark-diaz/), [Vinodkumar Prabhakaran](https://md.research.elamur.ai/free/en/authors/vinodkumar-prabhakaran/). 4 papers in our library · 2020–2026 [OpenAlex](https://openalex.org/A5000967986) · [ORCID](https://orcid.org/0000-0002-7013-1810) · [Wikidata](https://www.wikidata.org/wiki/Q130836905) · [**h-index 17**](https://openalex.org/authors/A5000967986) · [54 works](https://openalex.org/works?filter=author.id:A5000967986) · [1,188 citations](https://openalex.org/works?filter=author.id:A5000967986&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/aida-mostafazadeh-davani/#papers) · [🔗 404 citations in our library](https://md.research.elamur.ai/free/en/authors/aida-mostafazadeh-davani/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Star rank #2267](https://md.research.elamur.ai/free/en/paper-stars/page/23/#star-2267) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Aida+Mostafazadeh+Davani) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Aida+Mostafazadeh+Davani) · [dblp](https://dblp.org/search?q=Aida+Mostafazadeh+Davani) · [ACL Anthology](https://aclanthology.org/search/?q=Aida+Mostafazadeh+Davani) ## Affiliations [🏢**Google** Mountain View · United States 2023–2026 · 10 papers*current*](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**University of Southern California** Los Angeles · United States 2019–2025 · 27 papers](https://md.research.elamur.ai/free/en/universities/university-of-southern-california/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Topics [Sentiment & emotion (1)](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Bias, fairness & safety (1)](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) ## Works by year ### 2020s [**2026** 1 paper](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/papers/kennedy-2020-contextualizing-hate-speech-classifiers-post/) ## Papers The most cited first (OpenAlex and our library). 1. **[Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/)** *(2022 · 415 citations)* In subjective tasks such as hate speech or emotion detection, labels from several annotators are usually merged into one gold label through majority vote or averaging. 2. **[Contextualizing Hate Speech Classifiers with Post-hoc Explanation](https://md.research.elamur.ai/free/en/papers/kennedy-2020-contextualizing-hate-speech-classifiers-post/)** *(2020 · 203 citations)* Hate speech classifiers trained on skewed data often treat group identifiers such as "black", "gay" or "muslim" as signs of hate on their own. 3. **[On Releasing Annotator-Level Labels and Information in Datasets](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/)** *(2021 · 168 citations)* NLP datasets with multiple annotators per item usually collapse those judgements into one label, by majority vote, averaging, or expert adjudication. 4. **[The Moral Foundations Reddit Corpus](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/)** *(2026 · 22 citations)* Large, hand-labelled corpora are needed to detect moral sentiment automatically, because the task is highly subjective. ## Also in our library 2 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 5 co-authors with a page, the most papers together first. - [Brendan Kennedy](https://md.research.elamur.ai/free/en/authors/brendan-kennedy/) ×2 - [Mark Díaz](https://md.research.elamur.ai/free/en/authors/mark-diaz/) ×2 - [Vinodkumar Prabhakaran](https://md.research.elamur.ai/free/en/authors/vinodkumar-prabhakaran/) ×2 - [Xiang Ren](https://md.research.elamur.ai/free/en/authors/xiang-ren/) - [Xisen Jin](https://md.research.elamur.ai/free/en/authors/xisen-jin/) ## Cited by Papers of our library cite their works 404 times (of the 140,884 papers whose bibliographies we read); [28 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/aida-mostafazadeh-davani/cited-by/).