Skip to main content
SpringerLink
Log in

Defining and Identifying Stophashtags in Instagram

  • Conference paper
  • First Online:
Advances in Big Data (INNS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

Included in the following conference series:

Abstract

Instagram could be considered as a tagged image dataset since it is reach in tags -known as hashtags- accompanying photos and, in addition, the tags are provided by photo owners/creators, thus, express in higher accuracy the meaning/message of the photos. However, as we showed in a previous study, only 30 % of Instagram hashtags are related with the visual content of the accompanied photos while the remaining 70 % are either related with other meta-communicative functions of the photo owner/creator or they are simply noise and are used mainly to increase photo’s localization and searchability. In this study we call the latter category of Instagram hashtags as ‘stophashtags’, inspired from the term ‘stopwords’ which is used in the field of computational linguistics to refer to common and non-descriptive words found in almost every text document, and we provide a theoretical and empirical framework through which stophashtags can be identified. We show that, in contrary to descriptive hashtags, stophashtags are characterized by high normalized subject (hashtag) frequency on irrelevant subject categories while normalized image frequency is also high.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The Top 20 Valuable Facebook Statistics. Online at: https://zephoria.com/top-15-valuable-facebook-statistics/.

  2. 2.

    Instagram: Stats. Online at: https://www.instagram.com/press/?hl=en.

  3. 3.

    http://www.crummy.com/software/BeautifulSoup/bs4/doc/.

  4. 4.

    http://www.surveymonkey.com/.

References

  1. Armano, G., Fanni, F., Giulian, A.: Stopwords identification by means of characteristic and discriminant analysis. In: Proceedings of the 7th International Conference on Agents Artificial Intelligence (ICAART 2015), pp. 353–360. Lisbon, Portugal (2015). doi:10.5220/0005194303530360

  2. Baranovic, M.: What #hashtags mean to mobile photography (2013). http://connect.dpreview.com/post/1256293279/hastag-photography

  3. Bramer, M.: Principles of Data Mining. Springer, London (2007)

    MATH  Google Scholar 

  4. Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: Nus-wide: a real-world web image database from National University of Singapore. In: Proceedings of ACM Conference on Image and Video Retrieval, pp. 368–375. Santorini, Greece (2009). doi:10.1145/1646396.1646452

  5. Daer, A.R., Hoffman, R., Goodman, S.: Rhetorical functions of hashtag forms across social media applications. Commun. Des. Q. Rev. 3(1), 12–16 (2014). doi:10.1145/2721882.2721884

    Article  Google Scholar 

  6. Drewe, N.: The Hilarious List of Hashtags Instagram Wont Let You Search. http://thedatapack.com/banned-instagram-hashtags-update/#more-171

  7. Fan, J., Shen, Y., Zhou, N., Gao, Y.: Harvesting large-scale weakly-tagged image databases from the web. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 802–809. San Francisco, United States, (2010). doi:10.1109/CVPR.2010.5540135

  8. Giannoulakis, S., Tsapatsoulis, N.: Instagram hashtags as image annotation meta-data. In: Proceedings of the 11th International Conference on Artificial Intelli-gence Applications and Innovations (AIAI’15), pp. 206-220. Bayonne, France (2015). doi:10.1007/978-3-319-23868-5_15

  9. Instagram’s New Guidelines Against Self-Harm Images & Accounts. In: Instagram Inc (2016). http://blog.instagram.com/post/21454597658/instagrams-new-guidelines-against-self-harm

  10. Jin, R., Chai, J.Y., Si, L.: Effective automatic image annotation via a coherent language model and active learning. In: Proceedings of the 12th ACM International Conference on Multimedia (ACM Multimedia 2004), pp. 892–899. New York, United States (2004). doi:10.1145/1027527.1027732

  11. Jin, C., Jin, S.-W.: Automatic image annotation using feature selection based on improving quantum particle swarm optimization. Sig. Process. 109, 172–181 (2015). doi:10.1016/j.sigpro.2014.10.031

    Article  Google Scholar 

  12. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). doi:10.1109/TSMC.1979.4310076

    Article  MathSciNet  Google Scholar 

  13. Sedhai, S., Sun, A.: HSpam14: a collection of 14 million tweets for hashtag-oriented spam research. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 223–232. Santiago, Chile (2015). doi:10.1145/2766462.2767701

  14. Snoek, C.G., Worring, M.: Concept-based video retrieval. Found. Trends Inf. Retrieval 2(4), 215–322 (2008). doi:10.1561/1500000014

    Article  Google Scholar 

  15. Tang, R., Zuo, J., Xu, K., Zheng, J., Wang, Y.: An intelligent semantic-based tag cleaner for folksonomies. In: Proceedings 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS2010), pp. 773–776. Guilin, China (2010). doi:10.1109/ICISS.2010.5657118

  16. Theodosiou, Z., Tsapatsoulis, N.: Crowdsourcing annotation: modelling keywords using low level features. In: Proceedings of the 5th International Conference on Internet Multimedia Systems Architecture and Application (IEEE IMSAA 2011), pp. 1–4. Bangalore, India (2011). doi:10.1109/IMSAA.2011.6156351

  17. Yang, H.-C., Lee, C.H.: Identifying spam tags by mining tag semantics. In: Proceedings 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), pp. 263–268. Macao, China (2011)

    Google Scholar 

  18. Zhu, G., Yan, S., Ma, Y.: Image tag refinement towards low-rank, content-tag prior and error sparsity. In: 18th ACM international conference on Multimedia (MM 2010), pp. 461–470. Firenze, Italy (2010). doi:10.1145/1873951.1874028

Download references

Author information

Authors and Affiliations

  1. Department of Communication and Internet Studies, Cyprus University of Technology, 30, Arch. Kyprianos str., CY-3036, Limassol, Cyprus

    Stamatios Giannoulakis & Nicolas Tsapatsoulis

Authors
  1. Stamatios Giannoulakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Tsapatsoulis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stamatios Giannoulakis .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Giannoulakis, S., Tsapatsoulis, N. (2017). Defining and Identifying Stophashtags in Instagram. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47898-2_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation