Work

Social Media and Language Processing: How Facebook and Twitter Provide the Best Frequency Estimates for Studying Word Recognition

Year: 2016

Type: article

Abstract: Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora (such as movie subtitles) are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times (up to 3.6% increase in explained variance). The results are robust (observed for Twitter- and Facebook-based frequencies on American English and British English datasets) and are still substantial when we control for corpus size.

Source: Cognitive Science

DOI: 10.1111/cogs.12392

Authors: Amaç Herdağdelen, Marco Marelli

Institutions: Meta (United Kingdom), Meta (United States), University of Trento

Language: English

Topic: Reading and Literacy Development

Subfield: Developmental and Educational Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,000

Funder: Universiteit Gent