Výsledky vyhledávání - "Tzagkarakis, Christos"

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Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments

Autor: Tzagkarakis, Christos, Charalampidis, Pavlos, Roubakis, Stylianos, Fragkiadakis, Alexandros, Ioannidis, Sotiris

Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and energy resources. To allevi

Externí odkaz: http://arxiv.org/abs/2206.07784

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Twitter Dataset on the Russo-Ukrainian War

Autor: Shevtsov, Alexander, Tzagkarakis, Christos, Antonakaki, Despoina, Pratikakis, Polyvios, Ioannidis, Sotiris

On 24 February 2022, Russia invaded Ukraine, also known now as Russo-Ukrainian War. We have initiated an ongoing dataset acquisition from Twitter API. Until the day this paper was written the dataset has reached the amount of 57.3 million tweets, ori

Externí odkaz: http://arxiv.org/abs/2204.08530

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Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

Autor: Shevtsov, Alexander, Tzagkarakis, Christos, Antonakaki, Despoina, Ioannidis, Sotiris

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interf

Externí odkaz: http://arxiv.org/abs/2112.04913

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Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections

Autor: Shevtsov, Alexander, Tzagkarakis, Christos, Antonakaki, Despoina, Ioannidis, Sotiris

Publikováno v: Software Impacts. 13:100333

This study introduces a novel, reproducible and reusable Twitter bot identification system. The system uses a machine learning (ML) pipeline, fed with hundreds of features extracted from a Twitter corpus. The main objective of the proposed ML pipelin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2305d945719865e3b7cb449ed866660
https://doi.org/10.1016/j.simpa.2022.100333

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