--- title: "Guido Zarrella — author profile and papers" description: "Guido Zarrella: 2 papers on Elamur Research (2011–2016), h-index 11 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/guido-zarrella/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/guido-zarrella/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Guido Zarrella # Guido Zarrella Guido Zarrella studies how computers can read short social media messages and recover what their authors leave unstated. Both papers here work with Twitter. One infers a user's gender from what the account posts and how it presents itself. The other judges whether a tweet's writer is for or against a given topic. A shared concern is the shortage of labeled examples. The work handles it by finding cheap sources of supervision in large amounts of unlabeled or loosely labeled text, and by favouring methods that do not depend on any single language. The most cited of these is [Discriminating Gender on Twitter](https://md.research.elamur.ai/free/en/papers/burger-2011-discriminating-gender-twitter/) (2011). It built a large multilingual training set by following profile links from Twitter accounts to blog platforms where users had picked a gender from a menu. That label was then carried back to the matching account, giving about 184,000 labeled users. Each user was described with plain Boolean n-gram features over words and characters, taken from tweets, names and profile descriptions with no language-specific processing, and several classifiers were compared on this representation. The later paper, [MITRE at SemEval-2016 Task 6](https://md.research.elamur.ai/free/en/papers/zarrella-2016-mitre-semeval-2016-task-6/) (2016), moves from author traits to opinion, a line close to the topic of [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/). It was an entry to a shared task on stance detection and relied on transfer learning. Word2vec skip-gram embeddings of words and phrases were learned from some 218 million unlabeled tweets posted in 2015. An LSTM layer was then pre-trained to predict topic hashtags that had been stripped from the messages, and the network was fine-tuned separately for each of five topics using little labeled data. Zarrella's papers carry the affiliation of MITRE in Bedford, United States, which appears on bylines from 2004 to 2024. 2 papers on this site · 2011–2016 Guido Zarrella has 2 papers on this site, in 2011–2016, most of them on [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/). [OpenAlex](https://openalex.org/A5030624104) · [ORCID](https://orcid.org/0000-0002-5770-0257) · [**h-index 11**](https://openalex.org/authors/A5030624104) · [37 works](https://openalex.org/works?filter=author.id:A5030624104) · [1 194 citations](https://openalex.org/works?filter=author.id:A5030624104&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works on this site](https://md.research.elamur.ai/free/en/authors/guido-zarrella/#papers) · [147 citations in our library](https://md.research.elamur.ai/free/en/authors/guido-zarrella/cited-by/) · 1 more work in our library · [⭐ Paper Star rank #2194](https://md.research.elamur.ai/free/en/paper-stars/page/22/#star-2194) 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=Guido+Zarrella) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Guido+Zarrella) · [dblp](https://dblp.org/search?q=Guido+Zarrella) · [ACL Anthology](https://aclanthology.org/search/?q=Guido+Zarrella) ## Affiliations - [Mitre (United States)](https://md.research.elamur.ai/free/en/companies/mitre/) · Bedford, United States —2004–2024 on 20 papers From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Sentiment & emotion 1](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) ## Works by year ### 2010s [**2016** 1 paper](https://md.research.elamur.ai/free/en/papers/zarrella-2016-mitre-semeval-2016-task-6/) · [**2011** 1 paper](https://md.research.elamur.ai/free/en/papers/burger-2011-discriminating-gender-twitter/) ## Papers The most cited first (OpenAlex and our library). 1. **[Discriminating Gender on Twitter](https://md.research.elamur.ai/free/en/papers/burger-2011-discriminating-gender-twitter/)** *(2011 · 631 citations)* The paper addresses automatically inferring the gender of Twitter users. Twitter profiles carry no demographic fields, and hand-labeled datasets had been small and English-only. The authors used distant supervision to build a large multilingual labeled set. Many Twitter users link from their profile URL to blog… 2. **[MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://md.research.elamur.ai/free/en/papers/zarrella-2016-mitre-semeval-2016-task-6/)** *(2016 · 223 citations)* The paper tackles stance detection in tweets: deciding whether a single message's author supports a topic, opposes it, or shows neither stance. ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Cited by Papers of our library cite their works 147 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/guido-zarrella/cited-by/).