| Title | Personality Extraction Through LinkedIn |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Piedboeuf, F., P. Langlais, and L. Bourg |
| Editor | Meurs, M-J., and F. Rudzicz |
| Conference Name | Advances in Artificial Intelligence |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-030-18305-9 |
| Abstract | LinkedIn is a professional social network used by many recruiters as a way to look for potential employees and communicate with them. In order to facilitate communication, it is possible to use personality models to gain a better understanding of what drives the person of interest. This paper first looks at the possibility of collecting a corpus on LinkedIn labelled with a personality model, which has never been done before, then looks at the possibility of extracting two different personalities from the user. We show that we can achieve results going from 73.7{%} to 80.5{%} of precision on the DiSC personality model and from 80.7{%} to 86.2{%} of precision on the MBTI personality model. These results are similar to what has been found on other social networks such as Facebook or Twitter, which is surprising given the more professional nature of LinkedIn. Finally, an analysis of the significance of the results and of the possible sources of errors is presented. |
