Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

By Daniele Quercia, Michal Kosinski, David J. Stillwell, Jon Crowcroft. 2011

532 citations recorded by the source corpus.

Subfields: Clinical Psychology, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

In Cognitive Science & Psychology

Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “Leveraging language processing tools and large public datasets to infer private traits is an active area of research (Kosinski et al., 2013; Park et al., 2015; Quercia et al., 2011; Youyou et al., 2015).”
Taxonomy of Risks posed by Language Models (2022, background): “Leveraging language processing tools and large public datasets to infer protected and other personal traits is an active area of research [107, 140, 146, 200], despite serious ethical concerns [6, 185].”
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (2019, background): “For instance, digital records of behavior such as Facebook ‘Like’ data [24, 33], Twitter profiles [42] or music collections [39] can be used to infer psychological traits and political orientation [24, 33, 52], while the merchants you pay to or webpages you visit are predictive for product interest ”
Computer-based personality judgments are more accurate than those made by humans (2015, background): “…believed that accurate personality perceptions stem from social-cognitive skills of the human brain, recent developments in machine learning and statistics show that computer models are also capable of making valid personality judgments by using digital records of human behavior (11 – 13).”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, background): “Previous studies on personality prediction in social media reveal a positive correlation between neuroticism and usage of anxiety- and anger-related words (Adali & Golbeck, 2012; Golbeck, Robles, & Turner, 2011; Quercia et al., 2011; Schwartz et al., 2013).”
The power of prediction with social media (2013, background): “In other words, one could exploit social media data as a kind of predictive market .”
From "I Love You Babe" to "Leave Me Alone" - Romantic Relationship Breakups on Twitter (2014, background): “However, there has been a body of work on inferring personality traits from Twitter usage [32,17,37,11].”
Ethical Research Protocols for Social Media Health Research (2017, background): “Public users may be aware that their tweets can be read by anyone, but posted messages may still be intended for their small group of followers (Hudson and Bruckman, 2004; Quercia et al., 2011; Neuhaus and Webmoor, 2012; O’Connor, 2013; Kandias et al., 2013).”
User-generated-content versus marketing-generated-content: personality and content influence on traveler’s behavior (2018, background): “On the other side, openness is also easy to be predicted in social media (Quercia et al., 2011).”
Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology (2014, background): “For instance, Quercia et al. (2011) analyzed the relationships between personality and different types of twitterers, finding that 2Just a few remarks about Twitter’s alleged representativeness and inherent biases.”