Abstract
Online Reputation Management systems aim at identifying and classifying Twitter influencers due to their importance for brands. Current methods mainly rely on metrics provided by Twitter such as followers, retweets, etc. In this work we follow the research initiated at RepLab 2014, but relying only on the textual content of tweets. Moreover, we have proposed a workflow to identify influencers and classify them into an interest group from a reputation point of view, besides the classification proposed at RepLab. We have evaluated two families of classifiers, which do not require feature engineering, namely: deep learning classifiers and traditional classifiers with embeddings. Additionally, we also use two baselines: a simple language model classifier and the “majority class” classifier. Experiments show that most of our methods outperform the reported results in RepLab 2014, especially the proposed Low Dimensionality Statistical Embedding.
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Notes
- 1.
Influencer is a user (person or brand) who may influence a high number of other people.
- 2.
In ancient Rome, auctoritas was the general level of prestige a person had. Due to that, authority in this context means prestige.
- 3.
- 4.
We did not include Stockholder and Investor classes because they did not have data for Automotive and in Banking they did not have either training or test data.
- 5.
- 6.
Sum has empirically given better results than average or concatenation.
- 7.
Sum has empirically given better results than average.
- 8.
Previously in other tasks as Low Dimensionality Representation (LDR).
- 9.
We have tested several machine learning algorithms and finally we report the ones with the best results.
- 10.
As the Undecidable class only appears in training set, we have removed it to avoid noise in the training phase.
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Acknowledgements
The work of the last author was funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project. Authors from Universitat Jaume I have been funded by the MINECO R&D project TIN2017-88805-R.
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Nebot, V., Rangel, F., Berlanga, R., Rosso, P. (2018). Identifying and Classifying Influencers in Twitter only with Textual Information. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_3
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