[u' @article{sanchez-corcuera_analysing_2021, title = {Analysing centralities for organisational role inference in online social networks}, volume = {99}, issn = {09521976}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197620303663}, doi = {10.1016/j.engappai.2020.104129}, abstract = {The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.}, language = {en}, urldate = {2021-01-04}, journal = {Engineering Applications of Artificial Intelligence}, author = {S\xe1nchez-Corcuera, Rub\xe9n and Bilbao-Jayo, Aritz and Zulaika, Unai and Almeida, Aitor}, month = mar, year = {2021}, keywords = {Adversarial information retrieval, Artificial Intelligence, Graph centralities, IF4.201, Information inference, Online social networks, Q1, machine learning, social network analysis, social networks}, pages = {104129}, } ']