Improving Gender Classification of Blog Authors

By Arjun Mukherjee, B. Liu. 2010

263 citations recorded by the source corpus.

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Citations in the corpus

Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs (2011, background): “, 2007), gender categorization (Koppel et al., 2002; Mukherjee and Liu, 2010; Herring and Paolillo, 2006), identification of interactional style (Jurafsky et al.”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “…the above-listed experiments have been conducted on Twitter, a variety of other datasets have been used, including phone conversations (Mairesse et al., 2007; Ivanov et al., 2011), blogs (Mukherjee and Liu, 2010; Schler et al., 2006), Facebook (Markovikj et al., 2013), or YouTube (Filippova, 2012).”
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification (2017, background): “, 2009), PoS tags (Koppel et al., 2002; Mukherjee and Liu, 2010), or patterns captured by context-free-grammar-derived linguistic patterns; see e.”
Semi-supervised Gender Classification with Joint Textual and Social Modeling (2016, background): “Applications developed from gender classification have enormous commercial value in personalization, marketing and judicial investigation (Mukherjee and Liu, 2010; Burger et al. , 2001; Volkova et al. , 2013).”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “Other related work is that of Otterbacher (2010), who studied stylistic differences between male and female reviewers writing product reviews, and Mukherjee and Liu (2010), who applied positive, negative and emotional connotation features for gender classification in microblogs.”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “Other works have found males use more formal, affirmation, and informational words, while females use more social interaction, and deictic language [33–36].”
Obfuscating Gender in Social Media Writing (2016, background): “, 2015), weblogs (Mukherjee and Liu, 2010; Schler et al., 2006; Yan and Yan, 2006) and user-review sites (Johannsen et al.”
Discriminating Gender on Twitter (2011, background): “Many latent features have been explored, but gender and age have generated great interest (Schler et al., 2006; Burger and Henderson, 2006; Argamon et al., 2007; Mukherjee and Liu, 2010; Rao et al., 2010).”
What Yelp Fake Review Filter Might Be Doing? (2013, methodology): “We consider two types of features: Deep syntax (Feng et al., 2012b) and POS sequence patterns (Mukherjee and Liu, 2010).”
How to Use less Features and Reach Better Performance in Author Gender Identification (2014, methodology): “It is also remarkable that nearly every combination of features we use achieves a higher accuracy than Mukherjee and Liu (2010)—a fact that tells us that the chosen features are deﬁnitely relevant features for gender iden-tiﬁcation.”
Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment (2014, background): “, 2011), blogs (Mukherjee and Liu, 2010; Schler et al., 2005), telephone conversations (Garera and Yarowsky, 2009), YouTube (Filippova, 2012) and chats in social networks (Peersman et al.”
Computational Sociolinguistics: A Survey (2016, methodology): “Others have created their own blog corpus from various sources including LiveJournal and Xanga (Burger and Henderson 2006; Mukherjee and Liu 2010; Nowson and Oberlander 2006; Rosenthal and McKeown 2011; Sarawgi, Gajulapalli, and Choi 2011; Yan and Yan 2006).”