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Content Description on a Mobile Image Sharing Service: Hashtags on Instagram  

Dorsch, Isabelle (Department of Information Science, Institute of Linguistics and Information Science, Heinrich Heine University Dusseldorf)
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Journal of Information Science Theory and Practice / v.6, no.2, 2018 , pp. 46-61 More about this Journal
The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.
image indexing; Instagram; knowledge organization; folksonomy; user behavior; tagging behavior;
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1 Maron, M. E. (1977). On indexing, retrieval and the meaning of about. Journal of the American Society for Information Science, 28(1), 38-43.   DOI
2 Messina, C. (2007). how do you feel about using # (pound) for groups. As in #barcamp [msg]? @chrismessina (Twitter). Retrieved May 2, 2017 from
3 Mohammad, S. M., & Kiritchenko, S. (2015). Using hashtags to capture fine emotion categories from Tweets. Computational Intelligence, 31(2), 301-326.   DOI
4 Nashmi, E. A. (2018). From selfies to media events: from selfies to media events: how Instagram users interrupted their routines after the Charlie Hebdo shootings. Digital Journalism, 6(1), 98-117.   DOI
5 Nov, O., Naaman, M., & Ye, C. (2008). What drives content tagging: The case of photos on Flickr. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1097-1100). New York: ACM.
6 Oh, C., Lee, T., Kim, Y., Park, S., & Suh, B. (2016). Understanding participatory hashtag practices on Instagram: a case study of weekend hashtag project. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 1280-1287). New York: ACM.
7 Ortony, A., & Turner, T. J. (1990). What's basic about basic emotions? Psychological Review, 97(3), 315-331.   DOI
8 Panofsky, E. (1955). Meaning in the visual arts. Garden City, NY: Doubleday Anchor Books.
9 Pegoraro, A., Comeau, G. S., & Frederick, E. L. (2018). #SheBelieves: the use of Instagram to frame the US women’s soccer team during #FIFAWWC. Sport in Society, 21(7), 1063-1077.   DOI
10 Peters, I. (2009). Folksonomies: Indexing and retrieval in Web 2.0. Berlin: De Gruyter Saur.
11 Allem, J.-P., Escobedo, P., Chu, K.H., Cruz, T. B., & Unger, J. B. (2017). Images of little cigars and cigarillos on Instagram identified by the hashtag #swisher: thematic analysis. Journal of Medical Internet Research, 19(7), e255.   DOI
12 Andalibi, N., Ozturk, P., & Forte, A. (2017). Sensitive selfdisclosures, responses, and social support on Instagram: the case of #depression. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1485-1500). New York: ACM.
13 Austin, J. L. (1962). How to do things with words. Oxford: Clarendon.
14 Beaudoin, J. (2007). Folksonomies: Flickr image tagging: patterns made visible. Bulletin of the American Society for Information Science and Technology, 34(1), 7-11.   DOI
15 Buarki, H., & Alkhateeb, B. (2018). Use of hashtags to retrieve information on the web. The Electronic Library, 36(2), 286-304.   DOI
16 Carrotte, E. R., Prichard, I., & Lim, M. S. C. (2017). “Fitspiration” on social media: A content analysis of gendered images. Journal of Medical Internet Research, 19(3), e95.   DOI
17 Coelho, R. L. F., de Oliveira, D. S., & de Almeida, M. I. S. (2016). Does social media matter for post typology? Impact of post content on Facebook and Instagram metrics. Online Information Review, 40(4), 458-471.   DOI
18 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Psychology Press.
19 Dennis, B. M. (2006). Foragr: Collaborative tagged photographs and social information visualisation. In Proceedings of the Collaborative Web Tagging Workshop at WWW 2006, Edinburgh, UK.
20 Daer, A. R., Hoffman, R. F., & Goodman, S. (2014). Rhetorical functions of hashtag forms across social media applications. Communication Design Quarterly Review, 3(1), 12-16.   DOI
21 Giannoulakis, S., & Tsapatsoulis, N. (2015). Instagram hashtags as image annotation metadata. In R. Chbeir, Y. Manolopoulos, I. Maglogiannis, & R. Ralhajj (Eds.), Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology (Vol. 458, pp. 206-220). Cham: Springer.
22 Peters, I., & Stock, W. G. (2007). Folksonomy and information retrieval. Proceedings of the American for Information Science and Technology, 44(1), 1-28.
23 Denton, E., Weston, J., Paluri, M., Bourdev, L., & Fergus, R. (2015). User conditional hashtag prediction for images. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1731-1740). New York: ACM.
24 Dorsch, I., Zimmer, F., & Stock, W. G. (2017). Image indexing through hashtags in Instagram. Proceedings of the Association for Information Science and Technology, 54(1), 658-659.
25 Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115.   DOI
26 Enser, P. G. B. (2008). Visual image retrieval. Annual Review of Information Science and Technology, 42(1), 1-42.   DOI
27 Gibbs, M., Meese, J., Arnold, M., Nansen, B., & Carter, M. (2015). #Funeral and Instagram: death, social media, and platform vernacular. Information, Communication & Society, 18(3), 255-268.   DOI
28 Golder, S. A., & Huberman, B. A. (2006). Usage patterns of collaborative tagging systems. Journal of Information Science, 32(2), 198-208.   DOI
29 Pluzhenskaia, M. (2006). Folksonomies or fauxsonomies: How social is social bookmarking? In Proceedings of the 17th ASIS&T SIG/CR Classification Research Workshop (pp. 23-24), Austin, TX.
30 Pila, E., Mond, J. M., Griffiths, S., Mitchison, D., & Murray, S. B. (2017). A thematic content analysis of #cheatmeal images on social media: Characterizing an emerging dietary trend. International Journal of Eating Disorders, 50(6), 698-706.   DOI
31 Rasmussen, E. M. (1997). Indexing images. Annual Review of Information Science and Technology (ARIST), 32, 169-196.
32 Rondeau, S. (2014). The life and times of aboutness: A review of the library and information science literature. Evidence Based Library and Information Practice, 9(1), 14-35.   DOI
33 Rorissa, A. (2010). A comparative study of Flickr tags and index terms in a general image collection. Journal of the American Society for Information Science and Technology, 61(11), 2230-2242.   DOI
34 Schmidt, S., & Stock, W. G. (2009). Collective indexing of emotions in images: a study in emotional information retrieval. Journal of the Association for Information Science and Technology, 60(5), 863-876.
35 Shatford, S. (1986). Analyzing the subject of a picture: a theoretical approach. Cataloging & Classification Quarterly, 6(3), 39-62.   DOI
36 Shatford Layne, S. (1994). Some issues in the indexing of images. Journal of the American Society for Information Science, 45(8), 583-588.   DOI
37 Shatford Layne, S. (2002). Subject access to art images. In M. Baca (Ed.), Introduction to art image access: Issues, tools, standarts, strategies (pp. 1-20). Los Angeles, CA: Getty Research Institute.
38 Hollenstein, L., & Purves R. (2010). Exploring place through user-generated content: using Flickr tags to describe city cores. Journal of Spatial Information Science, 1(1), 21-48.
39 Guidry, J. D., Messner, M., Jin, Y., & Medina-Messner, V. (2015). From #mcdonaldsfail to #dominossucks: an analysis of Instagram images about the 10 largest fast food companies. Corporate Communications: An International Journal, 20(3), 344-359.   DOI
40 Halavais, A. (2014). Structure of Twitter: Social and technical. In K. Weller, A. Bruns, J. Burgess, N. Mahrt, & C. Puschmann (Eds.), Twitter and society (pp. 29-41). New York: Peter Lang.
41 Holmberg, C., Chaplin, J. E., Hillman, T., & Berg, C. (2016). Adolescents' presentation of food in social media: an explorative study. Appetite, 99, 121-129.   DOI
42 Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288.   DOI
43 Hu, Y., Manikonda, L., & Kambhampati, S. (2014). What we Instagram: A first analysis of Instagram photo content and user types. In Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (pp. 595-598). Palo Alto, CA: AAAI Press.
44 Ingwersen, P. (2002). Cognitive perspectives of document representation. In H. Bruce, R. Fidel, P. Ingwersen, & P. Vakkari (Eds.), Proceedings of the 4th International Conference on Conceptions of Library and Information Science (pp. 285-300). Greenwood Village, CO: Libraries Unlimited.
45 Instagram. (2010). Instagram launches. Retrieved April 14, 2017 from
46 Instagram. (2018). Instagram statistics. Retrieved Mar 5, 2018 from
47 Souza, F., de Las Casas, D., Flores, V., Youn, S., Cha, M., Quercia, D., & Almeida, V. (2015). Dawn of the selfie era: the whos, wheres, and hows of selfies on Instagram. In Proceedings of the 2015 ACM on Conference on Online Social Networks (pp. 221-231). New York: ACM.
48 Sheldon, P., & Bryant, K. (2016). Instagram: Motives for its use and relationship to narcissism and contextual age. Computers in Human Behavior, 58, 89-97.   DOI
49 Siebenlist, T. (2013). Emotionale suche. In D. Lewandowski (Ed.), Handbuch Internet-Suchmaschinen (pp. 299-327). Heidelberg: Akademische Verlagsgesellschaft.
50 Smith, G. (2004). Folksonomy: Social classification. Retrieved May 9, 2018 from folksonomy_social_classification.html.
51 Stock, W. G., & Stock, M. (2013). Handbook of information science. Berlin: De Gruyter Saur.
52 Stvilia, B., & Jorgensen, C. (2010). Member activities and quality of tags in a collection of historical photographs in Flickr. Journal of the American Society for Information Science and Technology, 61(12), 2477-2489.   DOI
53 Turner, J. M. (1995). Comparing user-assigned terms with indexer-assigned terms for storage and retrieval of moving images: Research results. In Proceedings of the 58th ASIS Annual Meeting, Vol 3. (pp. 9-12). Medford, NJ: Information Today.
54 Vander Wal, T. (2007). Folksonomy coinage and definition. Retrieved Apr 16, 2017 from
55 Veszelszki, A. (2016). #time, #truth, #tradition: an imagetext relationship on Instagram: Photo and hashtag. In A. Benedek, & A. Veszelszki (Eds.), In the beginning was the image: the omnipresence of pictures: time, truth, tradition (pp. 93-113). New York: Peter Lang.
56 Kipp, M. E. I. (2006). @toread and cool: Tagging for time, task and emotion. In Proceedings of the 17th Annual ASIS&T SIG/CR Classification Research Workshop, Austin, TX.
57 Jorgensen, C. (2002). Image access, the semantic gap, and social tagging as a paradigm shift. In J. Lussky (Ed), Proceedings of the 18th Workshop of the American Society for Information Science and Technology Special Interest Group in Classification Research, Milwaukee, WI.
58 Jorgensen, C. (2003). Image retrieval: Theory and research. Lanham, MD: Scarecrow Press.
59 Yanbe, Y., Jatowt, A., Nakamura, S., & Tanaka, K. (2007). Can social bookmarking enhance search in the web? In Proceedings of the 7th ACM/IEEE-CS joint Conference on Digital Libraries (pp. 107-116). New York: ACM.
60 Ye, Z., Hashim, N. H., Baghirov, F., & Murphy J. (2018). Gender differences in Instagram hashtag use. Journal of Hospitality Marketing & Management, 27(4), 386-404.   DOI
61 Kipp, M. E. I., & Campbell, D. G. (2006). Patterns and inconsistencies in collaborative tagging systems: an examination of tagging practices. Proceedings of American Society for Information Science and Technology, 43(1), 1-18.
62 Knautz, K. (2012). Emotion felt and depicted: consequences for multimedia retrieval. In D. R. Neal (Ed.), Indexing and retrieval of non-text information (pp. 343-375). Berlin: De Gruyter Saur.
63 Kremerskothen, K. (2012). An amazing 8 years. Retrieved April 16, 2017 from
64 Krippendorff, K. (2004). Content analysis: an introduction to its methodology (2nd ed). Thousand Oaks, CA: Sage Publications.
65 Lancaster, F. W. (2003). Indexing and abstracting in theory and practice. Champaign, IL: University of Illinois, Graduate School of Library and Information Science.
66 MacQueen, K. M., McLellan, E., Kay, K., & Milstein, B. (2009). Codebook development for team-based qualitative analysis. In K. Krippendorf, & M. A. Bock (Eds.), The content analysis reader (pp. 211-219). Los Angeles: Sage Publications.
67 Marcus, S.-R. (2016). Thinspiration vs. thicksperation: comparing pro-anorexic and fat acceptance image posts on a photo-sharing site. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 10(2).