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Position Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 31

Evaluation without Ground Truth: a Comparative Study on Preference Mining Techniques in Twitter Social Network

DOI: http://dx.doi.org/10.15439/2022F280

Citation: Position Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 31, pages 6568 ()

Full text

Abstract. In social media research the lack of ground truth for evaluation is a recurrent problem. We study the preference mining task in Twitter network which suffers from this lack of ground truth problem. We implement three different methods from literature, considering a common preference domain of news and carry a comparative study among them. Our preliminary findings show that is possible to combine methods in order to avoid unfeasible user surveying baselines and enable the evaluation of techniques. In the future, our target is to completely eliminate ground truth sets and evaluate based on correlation and causality techniques.


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