[u' @article{sanchez-corcuera_analyzing_2021, title = {Analyzing the {Existence} of {Organization} {Specific} {Languages} on {Twitter}}, volume = {9}, issn = {2169-3536}, url = {https://ieeexplore.ieee.org/document/9507509/}, doi = {10.1109/ACCESS.2021.3102865}, abstract = {The presence of organisations in Online Social Networks (OSNs) has motivated malicious users to look for attack vectors, which are then used to increase the possibility of carrying out successful attacks and obtaining either private information or access to the organisation. This article hypothesised that organisations have speci\ufb01c languages that their members use in OSNs, which malicious users could potentially use to carry out an impersonation attack. To prove these speci\ufb01c languages, we propose two tasks: classifying tweets in isolation by their author\u2019s organisation and classifying users\u2019 entire timelines by organisation. To accomplish both tasks, we generate a dataset of over 15 million tweets of \ufb01ve organisations, and we apply language dependant models to test our hypothesis. Our results and the ablation study conclude that it is possible to classify tweets and users by organisation with more than three times the performance achieved by a traditional ML algorithm, showing a substantial potential for predicting the linguistic style of tweets.}, language = {en}, urldate = {2021-09-01}, journal = {IEEE Access}, author = {Sanchez-Corcuera, Ruben and Zubiaga, Arkaitz and Almeida, Aitor}, year = {2021}, keywords = {Adversarial information retrieval, Artificial Intelligence, IF3.367, Information inference, Natural language processing, Online social networks, Q2, Social networks, machine learning, nlp, social network analysis}, pages = {111463--111471}, } ']