Social Media Is NOT that Bad! The Lexical Quality of Social Media
Proceedings of the International AAAI Conference on Web and Social MediaPublished 3 August 2021Open access
Luz Rello, Ricardo Baeza‐Yates
Citations9
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
An updated and complete analysis of the lexical quality of Social Media written in English and Spanish, including how lexicalquality changes in time is presented.
Abstract
There is a strong correlation between spelling errors and web text content quality. Using our lexical quality measure, based in a small corpus of spelling errors, we present an estimation of the lexical quality of the main Social Media sites. This paper presents an updated and complete analysis of the lexical quality of Social Media written in English and Spanish, including how lexical quality changes in time.
Keywords
Computer ScienceSocial Sciences
Business HorizonsUsers of the world, unite! The challenges and opportunities of Social Media
17,519 Citations2009Andreas Kaplan, Michael Haenlein
A classification of Social Media is provided which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds.
Finding high-quality content in social media
1,237 Citations2008Eugene Agichtein, Carlos Castillo +3 more
This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.
interactionsWeb content accessibility guidelines 1.0
990 Citations2001Wendy Chisholm, Gregg C. Vanderheiden +1 more
These guidelines explain how to make Web content accessible to people with disabilities and explain how to make multimedia content more accessible to a wide audience.
Predictors of answer quality in online Q&A sites
411 Citations2008F. Maxwell Harper, Daphne R. Raban +2 more
It is found that a Q&A site's community of users contributes to its success, and it was typically higher in Google Answers (a fee-based site) than in the free sites the authors studied, and paying more money for an answer led to better outcomes.
A framework to predict the quality of answers with non-textual features
352 Citations2006Jiwoon Jeon, W. Bruce Croft +2 more
This paper presents a framework to use non-textual features to predict the quality of documents and shows the quality measure can be successfully incorporated into the language modeling-based retrieval model.
Facts or friends?
262 Citations2009F. Maxwell Harper, Daniel Moy +1 more
The use of machine learning techniques are explored to automatically classify questions as conversational or informational, learning in the process about categorical, linguistic, and social differences between different question types.
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
178 Citations2009Jiang Bian, Yandong Liu +3 more
Results of a large scale evaluation demonstrate that the semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation and quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.
Lecture notes in computer scienceContent Quality Assessment Related Frameworks for Social Media
88 Citations2009Kevin Chai, Vidyasagar Potdar +1 more
A comprehensive review of 19 existing CQ assessment related frameworks for social media in addition to proposing directions for framework improvements is presented.
Estimating dyslexia in the web
26 Citations2011Ricardo Baeza‐Yates, Luz Rello
A classification of lexical errors is proposed and unique dyslexic errors are distinguished from other kind of errors due to spelling and grammatical errors, typos, OCR errors and errors produced when English is used as a foreign language.
Lexical quality as a proxy for web text understandability
22 Citations2012Luz Rello, Ricardo Baeza‐Yates
It is shown that a recently introduced lexical quality measure is also valid to measure textual Web accessibility and carried out a user study using eye tracking to prove that the degree of lexicalquality of a text is related to the level of understandability of atext, one of the factors behind Web accessibility.
A "quick and dirty" website data quality indicator
17 Citations2008Irit Askira Gelman, Anthony L. Barletta
A simple, "quick and dirty" metric for assisting in the evaluation of the quality of websites is proposed, which utilizes the reported hit counts of search engine queries on a pre-determined set of commonly misspelled words.
On measuring the lexical quality of the web
15 Citations2012Ricardo Baeza‐Yates, Luz Rello
A measure for estimating the lexical quality of the Web, that is, the representational aspect of the textual web content, based in a small corpus of spelling errors is proposed and applied to English and Spanish.
Proceedings of the International AAAI Conference on Web and Social MediaHow Bad Do You Spell?: The Lexical Quality of Social Media
8 Citations2021Ricardo Baeza‐Yates, Luz Rello
It is found that blogs and social networks are the main players and also the main contributors to the bad lexical quality of the Web, and that their quality is one order of magnitude worse than high quality sites.