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Developing a Network-Based Method for Building Associative Series of Hashtags

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  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1086))

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Abstract

Nowadays hashtags are an important mechanism of semantic navigation in social media. In the current research we considered the problem of building associative series of hashtags for Instagram. These series should meet two criteria: they should be relatively short and should not have wide semantic gaps between sequential hashtags. A generator of such series could be used for creating recommendations for increasing the quantity of hashtags in messages.

We gathered information from approximately 15 million messages from Instagram and built a co-occurrence network for hashtags from these messages. To build associative series we defined a formal definition of the semantic path building problem as a multicriteria optimization problem on the co-occurrence network.

We developed a combined optimization function for both criteria from the semantic path building problem. For measuring semantic similarity between hashtags, we use a metric based on vector embedding of hashtags by word2vec algorithm. Using empirical paths built by different algorithms, we graduated the parameters of the combined optimization function. The function with these parameters could be used in Dijkstra’s algorithm and in a specialized greedy algorithm for building semantic paths which look as appropriate associative series.

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Author information

Authors and Affiliations

  1. Financial University under the Government of the Russian Federation, 49 Leningradsky Prospekt, GSP-3, 125993, Moscow, Russia

    Sergey Makrushin & Nikita Blokhin

Authors
  1. Sergey Makrushin
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  2. Nikita Blokhin
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Corresponding author

Correspondence to Sergey Makrushin .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Institute of Mathematics and Mechanics Yekaterinburg, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Makrushin, S., Blokhin, N. (2020). Developing a Network-Based Method for Building Associative Series of Hashtags. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-39575-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39574-2

  • Online ISBN: 978-3-030-39575-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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