The mobile social networking application lnstagram 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 ofthis study is to analyze how lnstagram 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 weil as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, lsness, Performativeness, Fakeness, "lnsta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 lnstagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQl : 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 Contentrelatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: ls there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on lnstagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of lnstagram users and is not limited to a specific hashtag or picture motive, like previous studies.

1. Hashtags on
lnstagram
Content Description on a Mobile Image Sharing Service
Isabelle Dorsch
D e p a r t m e n t o f I n f o r m a t i o n S c i e n c e
H e i n r i c h H e i n e U n i v e r s i t y , D ü s s e l d o r f , G e r m a n y
1 2 t h S e p t e m b e r 2 0 1 8
Ta l k , I S I 5 3 0 2 K n o w l e d g e O r g a n i z a t i o n S e m i n a r

2. Hashtags on Instagram | Dorsch
Change of Media and Celebrities
2
“A picture is worth a thousand words”

3. Hashtags on Instagram | Dorsch 2
How do users index a
picture on Instagram with
at most 30 hashtags?

4. Hashtags on Instagram | Dorsch 4
Indexing of Pictures
2 approaches
Features extraction
e.g. color, shape, texture
Textual description
Content-based Concept-based
(Lancaster, 2003; Rasmussen, 1997)

5. Hashtags on Instagram | Dorsch 5
Folksonomy
Term for the free allocation of keywords (called tags)
(Smith, 2004, Vander Wal, 2007)
Total quantity of all assigned tags in an information service
(Peters, 2009)
Example: flickr
Folksonomy”  “folk” and “taxonomy”
https://www.flickr.com/

6. Hashtags on Instagram | Dorsch 6
Hashtags
Twitter: https://twitter.com/chrismessina/status/223115412
ChrisMessina as
“creator” of the hashtag

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Instagram
https://www.instagram.com
Main purpose:
Sharing photos and videos
Launched in October 2010
 With 25,000 signed-up users
 Only for iOS-Users
Since April 2012:
Also for android users
Now
 1B+ monthly active users
 500M+ daily active users
Instagram history: https://www.instagram.com/p/BaD1n2gjj1a/?hl=de&tagged=cat

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Instagram
 Posted architecture picture by kajaf on Instagram

9. RQ1: Are there any differences in relative frequencies of hashtags in the
picture categories?
RQ2: Given a picture category, what is the distribution of hashtag
categories; and given a hashtag category, what is the distribution of
picture categories?
RQ3: Is there any association between image categories and hashtag
categories?
Hashtags on Instagram | Dorsch 9
Research Questions

10. Hashtags on Instagram | Dorsch 10
Research Model
RQ1
RQ2+3
Instagram
User Behavior
Hashtag
Categories
Picture
Categories
Hashtag
Counts
Post
Picture with
Hashtags

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Methods Overview
Analysis of 14,649 hashtags for in total 1,000 Instagram pictures
+ Additional pretest with 50 Instagram pictures
Content Analysis: Development of 2 codebooks
Codebook categories: Picture categories & hashtag categories
Data collection & coding process

12.  Selection of 10 picture categories based on Hu et al. (2014)
 Development of a picture category codebook
Hu et al. (2014) 8 popular Instagram photo categories:
Hashtags on Instagram | Dorsch 12
Methods
FoodActivity
Captioned
Photo
Fashion
SelfieFriends Gadget Pet

13.  Selection of 10 picture categories based on Hu et al. (2014)
 Development of a picture category codebook
10 picture categories used for this study:
Hashtags on Instagram | Dorsch 13
Methods
FashionActivity Art
Captioned
Photo
SelfieFood Landscape Pet
Architecture
Friends
Gadget
Why these 10 categories?
Reason: Pretest

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Methods
Examples for the used picture categories
Activity

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Methods
Examples for the used picture categories
Architecture

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Methods
Examples for the used picture categories
Art

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Methods
Examples for the used picture categories
Captioned
Photo

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Methods
Examples for the used picture categories
Fashion

19. Hashtags on Instagram | Dorsch 19
Methods
Examples for the used picture categories
Food

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Methods
Examples for the used picture categories
Friends

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Methods
Examples for the used picture categories
Landscape

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Methods
Examples for the used picture categories
Pet

23. Hashtags on Instagram | Dorsch 23
Methods
Examples for the used picture categories
Selfie

24.  Selection of 7 hashtag categories
 Development of a hashtag category codebook
7 hashtag categories used for this study:
Hashtags on Instagram | Dorsch 24
Methods
Isness
Content-
relatedness
Emotiveness Fakeness
“Insta”-Tags Performativeness
Sentences

25. Content-relatedness
 Everything a picture directly or abstractly depicts
 Based on definitions of aboutness and ofness
(Shatford, 1986, Shatford Layne, 1994)
Hashtag examples:
If they are depicted:
#food, #person, #pet, #yellow, #colorful, #activity, #eyes, friends,
#bestfriends, #paris, #blackcats #polishgirl
Hashtags on Instagram | Dorsch 25
Methods

26. Emotiveness
 Includes all hashtags which contain primary emotions or feelings:
 Love
 Happiness
 Fun
 Surprise
 Aspiration
 Besides, consideration of all possible manifestations of emotions a user
could formulate
Hashtag examples:
#love, #fun, #happy, #sad, #happygirl, #doglover
Hashtags on Instagram | Dorsch 26
Methods
(Siebenlist, 2013)
 Sadness
 Anger
 Disgust
 Fear
 Shame

27. Sentences
 Includes all hashtags which are formulated as a whole sentences
 Sentences represent content
 Why not include them into the category Content-relatedness?
Hashtags on Instagram | Dorsch 27
Methods

28. Excursion: Indexing and
summarizing in knowledge
representation
Hashtags on Instagram | Dorsch 28
Methods
(Stock & Stock, 2013)

29. Sentences
 Includes all hashtags which are formulated as a whole sentences
 Category Content-relatedness: Only terms and phrases (Indexing)
 Category Sentences: Information condensation (Abstracts)
 According to this, a separation was made
Hashtag examples:
#WIWT  #WhatIWoreToday, #IFeelRealyBadToday
Hashtags on Instagram | Dorsch 29
Methods

30. Isness (Ingwersen, 2002)
 All non-topical features of a document; for pictures:
 Technical aspects of the photograph
(e.g., camera type, length of exposure, aperture) (Stock & Stock, 2013)
 (User)name of the photographer
 Date
 Location
 Type of the picture (e.g. selfie)
 Requirement: All of them not depicted on the picture posting
 Special consideration for art pictures and people
Hashtag examples:
#is_made_with_XY, #picoftheday, #photooftheday, #foodie, #photo, #nofilter,
#foodporn, #foodgasm, #blogger, #sunday, #winter , #selfietime
Hashtags on Instagram | Dorsch 30
Methods

31. Performativeness (Peters & Stock, 2007)
 Includes all hashtags for action requests or challenges on Instagram or
other platforms, e.g.:
 Contests like “participatory hashtag projects” on Instagram
 Account features
 Participation through tagging a picture with a certain hashtag
 General performative hashtags were not categorized (e.g. #petoftheday)
Hashtag examples:
#like4like, #likethis, #eatrealfood, #icebucketchallenge, #discover, #followme
Hashtags on Instagram | Dorsch 31
Methods

32. Fakeness
 Deliberately incorrectly assigned hashtags
 Includes all hashtags that are not valid for the respective image or posting
description in any way
 Except
 Hashtag provides room for interpretation  no clear false/true statement possible
 Typos
Hashtag examples:
#nature, #mountains, #beautiful, #latergram,
#naturelovers, #switzerland,
#cityscape, #selfie, #instanature
Hashtags on Instagram | Dorsch 32
Methods

33. “Insta”-Tags
 Contains as hashtag component “Insta,” “gram,” Instagram or a shortcut
regarding to the previously stated expressions
 Platform-specific phenomenon
Hashtag examples:
#instadaily, #instapic, #instaart, #instacat, #webstagram, #webstagramers,
#Igers, #naturegram, #latergram
Hashtags on Instagram | Dorsch 33
Methods

34. Hashtags on Instagram | Dorsch 34
Methods
Content-relatedness
#cat
Emotiveness
#happy
“Insta”-Tags
#catsofinstagram
Isness
#photooftheday
Performativeness
#excellent_cats
Sentences
#ilovemycat
Fakeness
e.g. #dog
Real world example for the used hashtag categories

35. Codebooks
1. Instagram picture category codebook
2. Instagram hashtag category codebook
Hashtags on Instagram | Dorsch 35
Methods
Purpose
Category overview
STRUCTURE
Common coding rules
Full code descriptions
References

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Methods
Example: Full code description
picture category codebook

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Methods
Example: Full code description
hashtag category codebook

38. Data collection
 November 2016 – January 2017
 Pretest: 50 pictures (manually)
 Analysis: 1,000 pictures (automatically + manually)
 Picture selection according to top Instagram hashtags of their category
(e.g. Category Food  #food 198m postings; #foods 8.5m postings)
Coding process
 2 coders, „4-eyes principle“
Hashtags on Instagram | Dorsch 38
Methods

39. RQ1: Frequencies of hashtags
Hashtags on Instagram | Dorsch 39
Results

40. RQ2: Relative frequency of hashtag categories by picture categories
Hashtags on Instagram | Dorsch 40
Results
Relative frequency of hashtag categories by picture categories (N=1,000 posts; 100 posts per picture category)

41. RQ3: Association between image and hashtag categories
 Chi-square test of independence
 A statistical association between hashtag categories and hashtag pictures
exists
 Picture categories affect hashtag categories
Hashtags on Instagram | Dorsch 41
Results

42. Evaluation of Instagram users‘ tagging behavior in a wide field:
Hashtags on Instagram | Dorsch 42
Discussion
On average 15 hashtags per picture
Content-relatedness + Isness hashtags were assigned the most
Less Emotiveness + Sentences hashtags
„Insta“-Tags  often assigned to the category Pet
Fakeness  often assigned to the category Captioned Photo
Small statistical association
RQ1
RQ2
RQ3

43. Limitations:
 Limited access to the data
Outlook:
 Users‘ intention?
 Analysis of Videos, Stories, etc.
 Gender-specific differences
 …
Hashtags on Instagram | Dorsch 43
Discussion

44. Please feel free to ask any
questions, etc. via Twitter
@bezwitschernd
i s a b e l l e . d o r s c h @ h h u . d e
T h a n k y o u f o r
y o u r a t t e n t i o n !
The full article:
Dorsch, I. (2018). Content Description on a Mobile Image Sharing Service: Hashtags on Instagram. Journal of Information Science Theory and Practice 6(2), 46-61.

45. If not directly indicated under the picture:
Slides 2, 4 from: Alexander Schöch
Slides 11, 25, 26, 29-31, 38, 42, 44 from: https://www.iconfinder.com/iconsets/streamline-icon-set-free-pack
author website: http://www.webalys.com/minicons/
Slide 38 from: https://www.iconfinder.com/iconsets/user-avatars-1
author website: https://usersinsights.com/
Slide 42 from: https://www.iconfinder.com/icons/2976459/bird_fly_pet_sparrow_icon
Slides 44 from: https://twitter.com
Instagram Screenshots: https://www.instagram.com (The author of the posting is indicated in the respective picture)
Further free pictures from: https://pixabay.com
Hashtags on Instagram | Dorsch 45
Picture Credits

46. 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.
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.
Instagram. (2010). Instagram launches. Retrieved Apr 14, 2017 from https://instagram-press.com/blog/2010/10/06/instagram-launches-2/
Instagram. (2018). Instagram statistics. Retrieved Sep 6, 2018 from https://instagram-press.com/our-story
Lancaster, F. W. (2003). Indexing and abstracting in theory and practice. Champaign, IL: University of Illinois, Graduate School of Library and Information Science.
Messina, C. (2007). how do you feel about using # (pound) for groups. As in #barcamp [msg]? @chrismessina (Twitter). Retrieved May 2, 2017 from
https://twitter.com/chrismessina/status/223115412.
Panofsky, E. (1955). Meaning in the visual arts. Garden City, NY: Doubleday Anchor Books.
Peters, I. (2009). Folksonomies: Indexing and retrieval in Web 2.0. Berlin: De Gruyter Saur.
Peters, I., & Stock, W. G. (2007). Folksonomy and information retrieval. Proceedings of the American for Information Science and Technology, 44(1), 1-28.
Rasmussen, E. M. (1997). Indexing images. Annual Review of Information Science and Technology (ARIST), 32, 169-196.
Shatford, S. (1986). Analyzing the subject of a picture: a theoretical approach. Cataloging & Classification Quarterly, 6(3), 39-62.
Shatford Layne, S. (1994). Some issues in the indexing of images. Journal of the American Society for Information Science, 45(8), 583-588.
Siebenlist, T. (2013). Emotionale suche. In D. Lewandowski (Ed.), Handbuch Internet-Suchmaschinen (pp. 299- 327). Heidelberg: Akademische Verlagsgesellschaft.
Smith, G. (2004). Folksonomy: Social classification. Retrieved May 9, 2018 from http://atomiq.org/%0Aarchives/2004/08/ folksonomy_social_classification.html
Stock, W. G., & Stock, M. (2013). Handbook of information science. Berlin: De Gruyter Saur.
Vander Wal, T. (2007). Folksonomy coinage and definition. Retrieved Apr 16, 2017 from http://www.vanderwal. net/folksonomy.html
Hashtags on Instagram | Dorsch 46
References