Classifying latent user attributes in twitter

By Delip Rao, David Yarowsky, Abhishek Shreevats, Manaswi Gupta. 2010

721 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Human-Computer Interaction

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “For instance, using data samples biased toward users whose gender (Rao et al., 2010) or political identity (Cohen and Ruths, 2013) are easy to discern, leads to overoptimistic performance estimations that do not reﬂect those obtained on balanced or representative samples) (Cohen and Ruths, 2013;…”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “…automatic age prediction has mostly been approached as a two-class or three-class classiﬁcation problem based on age spans with for example boundaries at 30 or 40 years (e.g. (Rao et al. 2010; Garera and Yarowsky 2009; Goswami, Sarkar, and Rustagi 2009)), thus corresponding to an etic approach.”
Gender as a Variable in Natural-Language Processing: Ethical Considerations (2017, background): “Rao et al. (2010) examined Twitter posts (“tweets”) to predict the gender categories they had ascribed to the texts’ authors.”
Challenges of studying and processing dialects in social media (2015, background): “A number of recent papers have explored social media with respect to sociolinguistic and dialectological questions (Rao et al., 2010; Eisenstein, 2013; Volkova et al., 2013; Doyle, 2014; Hovy et al., 2015; Volkova et al., 2015; Johannsen et al., 2015; Hovy and Søgaard, 2015; Eisenstein, to appear).”
Confounds and Consequences in Geotagged Twitter Data (2015, background): “…difference is statistically significant (p < .05, t = 3.2).4 3Binning is often employed in work on text-based age prediction (Garera and Yarowsky, 2009; Rao et al., 2010; Rosenthal and McKeown, 2011); it enables word and name counts to be shared over multiple ages, and avoids the complexity…”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, background): “Examples range from location [5], gender [6], age [7, 8], personality [9] or political preference [10, 11].”
Gender Inference of Twitter Users in Non-English Contexts (2013, methodology): “In prior work, e.g. (Rao et al., 2010; Pennacchiotti and Popescu, 2011; Za-mal et al., 2012), the dominant way of obtaining datasets consisting of Twitter users with high-conﬁdence gender-labels is to use gender-name associations.”
BlurMe: inferring and obfuscating user gender based on ratings (2012, methodology): “Nevertheless, there has been a fair amount of work indicating that user demographics are correlated to, and thus can be inferred from, user activity on social networks [9], blogs [2], and microblogs [12] etc.”
Gender identity and lexical variation in social media (2012, methodology): “Rao et al. (2010) trained a classifier on a dataset of posts (‘tweets’) by 1,000 authors.”
Predicting age groups of Twitter users based on language and metadata features (2017, background): “In fact, Sloan et al. [21] and Rao et al. [18] suggest that combining the predictive power of metadata with linguistics may be more powerful than either approach alone; researchers could potentially increase the application and utility of the age prediction tools by also examining language use…”
Discriminating Gender on Twitter (2011, methodology): “Rao et al. (2010) report results of 68.7% accuracy on gender from tweet texts alone using an ngram-only model, rising to 72.3 with hand-crafted “sociolinguistic-based” features.”
Secure Function Evaluation Using an FPGA Overlay Architecture (2017, background): “3021746 mining has given rise to significant privacy concerns, extensively documented by researchers [20, 24, 26, 30, 34, 36, 40] as well as the popular press [2, 42].”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “Other researchers included subjective patterns as features for gender classification of Twitter users (Rao et al., 2010).”
Characterizing Silent Users in Social Media Communities (2015, methodology): “Unlike many existing user proﬁling works that exclude lurkers from their empirical studies due to their inadequate content data (Rao et al. 2010; Nguyen et al. 2013), we propose to utilize their neigh-bors’ (their one-hop connected users) content to infer latent attributes including marital status,…”
Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors (2012, methodology): “In this paper, we use the signal present in a Twitter user’s neighborhood to infer attributes of that Twitter user herself.”
Predicting Twitter User Demographics from Names Alone (2018, background): “Numerous existing systems automatically infer missing demographics, such as gender, ethnicity, age and location (Mislove et al., 2011; Burger et al., 2011; Culotta et al., 2015; Pennacchiotti and Popescu, 2011; Rao et al., 2010; Jurgens et al., 2015; Dredze et al., 2013; Rout et al., 2013).”
Computational Sociolinguistics: A Survey (2016, methodology): “, male/female hygiene products, sororities) [220].”
Zooming in on Gender Differences in Social Media (2016, background): “Work has also been done on Twitter data, where tweets are used to predict several profile features, including gender (Rao et al., 2010; Burger et al., 2011).”