https://doi.org/10.1140/epjds/s13688-023-00383-9
Regular Article
Experimental evaluation of baselines for forecasting social media timeseries
1
University of South Florida, 4202 E. Fowler Ave, 33620, Tampa, FL, USA
2
Maastricht University, Minderbroedersberg 4-6, 6211 LK, Maastricht, The Netherlands
e
a.iamnitchi@maastrichtuniversity.nl
Received:
11
May
2022
Accepted:
28
February
2023
Published online:
27
March
2023
Forecasting social media activity can be of practical use in many scenarios, from understanding trends, such as which topics are likely to engage more users in the coming week, to identifying unusual behavior, such as coordinated information operations or currency manipulation efforts. To evaluate a new approach to forecasting, it is important to have baselines against which to assess performance gains. We experimentally evaluate the performance of four baselines for forecasting activity in several social media datasets that record discussions related to three different geo-political contexts synchronously taking place on two different platforms, Twitter and YouTube. Experiments are done over hourly time periods. Our evaluation identifies the baselines which are most accurate for particular metrics and thus provides guidance for future work in social media modeling.
Key words: Forecasting / Social media time series / Baselines
© The Author(s) 2023
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