Predicting Personality Using Novel Mobile Phone-Based Metrics

Montjoye, Yves-Alexandre de; Quoidbach, Jordi; Robic, Florent; Pentland, Alex

doi:10.1007/978-3-642-37210-0_6

Cited by 254 publications

(228 citation statements)

References 26 publications

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“…IBM listed some potential applications of personality prediction as follows: Targeted marketing, customer acquisition, customer care, personal connections [39]. In addition to the Facebook and Twitter data usage for the personality prediction, some researchers developed personality prediction models based on mobile phone logs [40], [41]. Another research group [42] showed that some visual patterns are correlated with personality traits of users and personality of a user can be predicted from the favorite images on Flickr online photo-sharing platform.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Cross-Cultural Personality Prediction based on Twitter Data

Çatal¹,

Song²,

Muratli³

et al. 2017

JSW

View full text Add to dashboard Cite

show abstract

“…Presently, predicting personality traits based on smart phone usage data accesses call logs, SMS logs, GPS logs, social networking services logs, use of internet, use of Bluetooth and battery [2], [4], [6]. In a previous work, authors have analyzed the relationship between smart phone usage and self-perceived personality with the help of applications usage logs, call logs and SMS logs.…”

Section: Related Workmentioning

confidence: 99%

“…Oliveira et al [4] suggested that, variables derived from the users' mobile phone call behavior as captured by call detail records and social network analysis of the call graph can be used to automatically infer the users' personality traits as defined by the Big Five model. Recently, some studies have tried to use GPS data with user personality traits [6], [9]. One of them focused on how to use GPS logs to identify personal mobility pattern with the collaboration of big five user personality [9].…”

Section: Related Workmentioning

confidence: 99%

“…One of them focused on how to use GPS logs to identify personal mobility pattern with the collaboration of big five user personality [9]. In another work, authors use GPS logs to predict big five user personality [6]. In this work, GPS logs are used to trace out the location of call (radius of gyration, number of places from which calls have been made) as a classification feature.…”

Section: Related Workmentioning

confidence: 99%

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Predicting User Attitude by Using GPS Location Clustering

Sokasane

Kim

2015

IEICE Trans. Inf. & Syst.

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SUMMARYIn these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.

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“…A data-driven approach might lead to an increased efficiency of text-based campaigns by being one step closer to delivering "the right offer to the right customer". For example, previous research showed that data-driven approaches can reliably predict mobile phone and Facebook user's personality [3][4][5]18], sexual orientation [6], or romantic relationships [7].…”

Section: Introductionmentioning

confidence: 99%

Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

Sundsøy

Bjelland

Iqbal

et al. 2014

Lecture Notes in Computer Science

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Abstract. This paper shows how big data can be experimentally used at large scale for marketing purposes at a mobile network operator. We present results from a large-scale experiment in a MNO in Asia where we use machine learning to segment customers for text-based marketing. This leads to conversion rates far superior to the current best marketing practices within MNOs.Using metadata and social network analysis, we created new metrics to identify customers that are the most likely to convert into mobile internet users. These metrics falls into three categories: discretionary income, timing, and social learning. Using historical data, a machine learning prediction model is then trained, validated, and used to select a treatment group. Experimental results with 250 000 customers show a 13 times better conversion-rate compared to the control group. The control group is selected using the current best practice marketing. The model also shows very good properties in the longer term, as 98% of the converted customers in the treatment group renew their mobile internet packages after the campaign, compared to 37% in the control group. These results show that data-driven marketing can significantly improve conversion rates over current best-practice marketing strategies.

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Predicting Personality Using Novel Mobile Phone-Based Metrics

Cited by 254 publications

References 26 publications

Cross-Cultural Personality Prediction based on Twitter Data

Cross-Cultural Personality Prediction based on Twitter Data

Predicting User Attitude by Using GPS Location Clustering

Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

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