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Prediction of brand stories spreading on social networks

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Abstract

Online social network is a major media for many types of information communication. Although the primary purpose of social networks is to connect people, they are more and more used in online marketing to connect businesses with customers as well as to connect customers amongst themselves. Brand stories generated by consumers or businesses can be easily and widely spread. As a result, those stories have a huge influence on the marketplace and indirectly affect the brand success. Understanding and modeling how a piece of information is spread on social media and its spreading level are crucial for business managers; not only to understand the information diffusion, but also for them to better control it. In this paper, we aim at developing models in order to predict the spread of brand stories on social networks, both in term of spreadability and spreading level. We applied several machine learning algorithms using three categories of features based on user-profile, temporal, and content of tweets. Experimental results on three tweet collections about brand stories reveal that our model significantly improves the prediction accuracy by about 4% compared to the related work.

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Notes

  1. https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/.

  2. https://www.dreamgrow.com/top-15-most-popular-social-networking-sites/.

  3. https://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/.

  4. Twitter is an online news and social networking service where people communicate in short messages called tweets.

  5. Institut de Recherche en Informatique de Toulouse, UMR5505 CNRS, France.

  6. Twitter Streaming API is documented on https://developer.twitter.com/en/docs/tweets/filter-realtime/guides/connecting.

  7. Twitter Search API is documented on https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets.

  8. BDpedia structures the information from Wikipedia pages; it can be queried using SPARQL to extract structured information locally stored in DBpedia or through an endpoint framework.

  9. https://pypi.python.org/pypi/holidays.

  10. http://dbpedia.org/snorql/.

  11. https://www.cs.york.ac.uk/semeval-2013/task2/index.html.

  12. https://pythonprogramming.net/new-data-set-training-nltk-tutorial/.

  13. http://weka.sourceforge.net/doc.stable/weka/classifiers/functions/LibLINEAR.html .

  14. https://osirim.irit.fr/ OSIRIM for Observatory of Systems Information Retrieval and Indexing of Multimedia contents is one of the IRIT platforms. It is a federative project mainly supported by the European Regional Development Fund (ERDF), the French Government, the Region Midi-Pyrénées and the National Center for the Scientific Research (CNRS).

  15. Synthetic Minority Over-sampling Technique: synthesises new minority instances by “taking each minority class sample and introducing synthetic examples along the line segments joining any/all of the k minority class nearest neighbors.” (Chawla et al. 2002).

  16. http://weka.sourceforge.net/doc.stable/weka/attributeSelection/PrincipalComponents.html.

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Acknowledgements

This work has been partially funded by the European Union’s Horizon 2020 H2020-SU-SEC-2018 under the Grant Agreement n\(^{\circ }\)833115 (PREVISION Project). However, this paper presents the paper authors’ own views.

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Authors and Affiliations

  1. IRIT, UMR5505 CNRS, Toulouse, France

    Thi Bich Ngoc Hoang

  2. University of Economics, The University of Danang, Da Nang, Vietnam

    Thi Bich Ngoc Hoang

  3. INSPE-UT2J, IRIT, UMR5505 CNRS, Université de Toulouse, Toulouse, France

    Josiane Mothe

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  1. Thi Bich Ngoc Hoang
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  2. Josiane Mothe
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Correspondence to Thi Bich Ngoc Hoang.

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Hoang, T.B.N., Mothe, J. Prediction of brand stories spreading on social networks. Adv Data Anal Classif 16, 559–591 (2022). https://doi.org/10.1007/s11634-021-00450-x

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