Predicting Personality from Twitter

Golbeck, Jennifer; Robles, Cristina; Edmondson, Michon; Turner, Karen

doi:10.1109/passat/socialcom.2011.33

Cited by 454 publications

(311 citation statements)

References 24 publications

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“…[7] and [8] built a personality prediction system for The Five Factor Model. A personality prediction system was also built for the Dark Triad personality model in [9].…”

Section: Iirelated Workmentioning

confidence: 99%

“…A personality prediction system was also built for the Dark Triad personality model in [9]. [7], [8], and [9] built the personality prediction system for English using tools such as LIWC (Linguistic Inquiry and Word Count) and MRC Psycholinguistic Database. Another study by [10] also used LIWC to build a personality prediction system on Facebook.…”

Section: Iirelated Workmentioning

confidence: 99%

See 1 more Smart Citation

Personality Prediction Based on Twitter Information in Bahasa Indonesia

Ong

Rahmanto

Williem

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active social media users such as Indonesia. Extraction of information from social media can yield insightful results if done correctly. Recent studies have managed to leverage associations between language and personality and build a personality prediction system based on those associations. The current study attempts to build a personality prediction system based on a Twitter user's information for Bahasa Indonesia, the native language of Indonesia. The personality prediction system is built on Support Vector Machine and XGBoost trained with 329 instances (users). Evaluation results using 10-fold cross validation shows that the system managed to reach highest average accuracy of 76.2310% with Support Vector Machine and 97.9962% with XGBoost.

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“…[7] and [8] built a personality prediction system for The Five Factor Model. A personality prediction system was also built for the Dark Triad personality model in [9].…”

Section: Iirelated Workmentioning

confidence: 99%

Section: Iirelated Workmentioning

confidence: 99%

Personality Prediction Based on Twitter Information in Bahasa Indonesia

Ong

Rahmanto

Williem

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Recently, researchers demonstrated that personality prediction can be performed by using historical textual features [8] and user profiles [9]. Most of the personality prediction studies used Facebook data [10]- [15] as it is the largest social networking platform in the world by the number of users (1,374,000,000 users) [16].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the personality prediction studies used Facebook data [10]- [15] as it is the largest social networking platform in the world by the number of users (1,374,000,000 users) [16]. Some researchers focused on Twitter data [6], [8], [9], [18]- [20] to predict the personality traits as Twitter is one of the most popular social networks which has 289 million users [17] all around the world. A recent study [21] proposed a personality prediction model which uses activities of users in Twitter and Instagram.…”

Section: Introductionmentioning

confidence: 99%

Cross-Cultural Personality Prediction based on Twitter Data

Çatal¹,

Song²,

Muratli³

et al. 2017

JSW

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Abstract:Social networking platforms such as Facebook, Twitter, YouTube, and Instagram, which generate a vast amount of data every second, emerged dramatically within the last ten years. This huge rich data provides crucial information about social interactions and human behaviour. Therefore, it is possible to identify the personality traits of a person by extracting and analysing relevant information from the social media. Recently, researchers demonstrated that personality prediction can be performed by using historical textual features and user profiles. While cross-cultural personality research is considered as a powerful tool to observe the differences in personality psychology, machine learning researchers analysing social media data did not perform research on the development of cross-cultural personality prediction models yet. In this paper, we propose a new research topic called cross-cultural personality prediction and discuss how these kinds of models can be built in practice. In the joint research project, we investigate whether there is a cultural or language difference in personality between people in two countries namely, South Korea and Turkey in Twitter. It will be particularly interesting because both of the countries are in Asia, but located in both ends of Asia and we'll investigate whether there is any meaningful personality difference in two countries or not. We argue that this kind of research will impact not only personality services such as IBM Personality Insight, but also cross-cultural personality psychology research area in the near future.

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“…Punctuation in particular has been used as a feature in a variety of tasks, including authorship identification (Chaski, 2005;Abbasi and Chen, 2005) and predicting users' gender (Bamman et al, 2012) and personality (Pennebaker et al, 2015;Golbeck et al, 2011). In addition to punctuation, there is some evidence that variation in capitalization is an important stylistic feature in informal computer-mediated communication (Ling, 2005).…”

Section: Introductionmentioning

confidence: 99%