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New Generation Computing

, Volume 35, Issue 4, pp 451–472 | Cite as

Predicting the Relevance of Social Media Posts Based on Linguistic Features and Journalistic Criteria

  • Alexandre Pinto
  • Hugo Gonçalo OliveiraEmail author
  • Álvaro Figueira
  • Ana Oliveira Alves
Special Feature
  • 308 Downloads

Abstract

An overwhelming quantity of messages is posted in social networks every minute. To make the utilization of these platforms more productive, it is imperative to filter out information that is irrelevant to the general audience, such as private messages, personal opinions or well-known facts. This work is focused on the automatic classification of public social text according to its potential relevance, from a journalistic point of view, hopefully improving the overall experience of using a social network. Our experiments were based on a set of posts with several criteria, including the journalistic relevance, assessed by human judges. To predict the latter, we rely exclusively on linguistic features, extracted by Natural Language Processing tools, regardless the author of the message and its profile information. In our first approach, different classifiers and feature engineering methods were used to predict relevance directly from the selected features. In a second approach, relevance was predicted indirectly, based on an ensemble of classifiers for other key criteria when defining relevance—controversy, interestingness, meaningfulness, novelty, reliability and scope—also in the dataset. The first approach achieved a F 1-score of 0.76 and an Area under the ROC curve (AUC) of 0.63. But the best results were achieved by the second approach, with the best learned model achieving a F 1-score of 0.84 with an AUC of 0.78. This confirmed that journalistic relevance can indeed be predicted by the combination of the selected criteria, and that linguistic features can be exploited to classify the latter.

Keywords

Relevance assessment Social mining Information extraction Natural language processing Automatic text classification 

Notes

Acknowledgements

This work was financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project REMINDS–UTAP-ICDT/EEI-CTP/0022/2014.

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Copyright information

© Ohmsha, Ltd. and Springer Japan 2017

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbra Portugal
  2. 2.CRACS / INESC TECUniversity of PortoPorto Portugal
  3. 3.IPC, Polytechnic Institute of CoimbraCoimbra Portugal

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