Classification of instagram fake users using supervised machine learning algorithms Article Swipe
Kristo Radion Purba
,
David Asirvatham
,
Raja Kumar Murugesan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v10i3.pp2763-2772
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v10i3.pp2763-2772
On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
Related Topics
Concepts
Computer science
Metadata
Social media
Random forest
Spamming
Machine learning
Influencer marketing
Class (philosophy)
Support vector machine
Artificial intelligence
Supervised learning
World Wide Web
Information retrieval
The Internet
Artificial neural network
Business
Marketing
Marketing management
Relationship marketing
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijece.v10i3.pp2763-2772
- http://ijece.iaescore.com/index.php/IJECE/article/download/21287/13855
- OA Status
- diamond
- Cited By
- 51
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2995230019
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2995230019Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.11591/ijece.v10i3.pp2763-2772Digital Object Identifier
- Title
-
Classification of instagram fake users using supervised machine learning algorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-08Full publication date if available
- Authors
-
Kristo Radion Purba, David Asirvatham, Raja Kumar MurugesanList of authors in order
- Landing page
-
https://doi.org/10.11591/ijece.v10i3.pp2763-2772Publisher landing page
- PDF URL
-
https://ijece.iaescore.com/index.php/IJECE/article/download/21287/13855Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ijece.iaescore.com/index.php/IJECE/article/download/21287/13855Direct OA link when available
- Concepts
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Computer science, Metadata, Social media, Random forest, Spamming, Machine learning, Influencer marketing, Class (philosophy), Support vector machine, Artificial intelligence, Supervised learning, World Wide Web, Information retrieval, The Internet, Artificial neural network, Business, Marketing, Marketing management, Relationship marketingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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51Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 12, 2023: 9, 2022: 12, 2021: 6Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2588298129, https://openalex.org/W2934898018, https://openalex.org/W2893495107, https://openalex.org/W209554261, https://openalex.org/W2595653137, https://openalex.org/W2194080939, https://openalex.org/W1849719402, https://openalex.org/W1978054023, https://openalex.org/W6758965246, https://openalex.org/W2734725703, https://openalex.org/W2887167794, https://openalex.org/W2536488151, https://openalex.org/W2125490153, https://openalex.org/W2013437453, https://openalex.org/W7020031218, https://openalex.org/W2793350103, https://openalex.org/W1976526581, https://openalex.org/W2950720316, https://openalex.org/W4293449511, https://openalex.org/W2071460053, https://openalex.org/W1136843918, https://openalex.org/W4235370517, https://openalex.org/W2914186518, https://openalex.org/W2594902547 |
| referenced_works_count | 24 |
| abstract_inverted_index.2 | 102, 104 |
| abstract_inverted_index.3 | 99 |
| abstract_inverted_index.4 | 107 |
| abstract_inverted_index.6 | 97 |
| abstract_inverted_index.a | 7, 16, 31, 41 |
| abstract_inverted_index.17 | 90 |
| abstract_inverted_index.On | 0 |
| abstract_inverted_index.an | 54 |
| abstract_inverted_index.be | 115 |
| abstract_inverted_index.is | 6 |
| abstract_inverted_index.of | 4, 19, 53, 120, 173 |
| abstract_inverted_index.on | 94 |
| abstract_inverted_index.to | 34, 49, 59, 70, 126, 160 |
| abstract_inverted_index.up | 159 |
| abstract_inverted_index.The | 76, 162 |
| abstract_inverted_index.and | 28, 64, 73, 85, 128, 130, 146, 179, 199 |
| abstract_inverted_index.are | 89, 122, 182 |
| abstract_inverted_index.for | 40, 141, 186 |
| abstract_inverted_index.pay | 35 |
| abstract_inverted_index.the | 2, 20, 51, 138, 142, 167, 183, 187 |
| abstract_inverted_index.Five | 110 |
| abstract_inverted_index.This | 56 |
| abstract_inverted_index.aims | 58 |
| abstract_inverted_index.also | 164 |
| abstract_inverted_index.fake | 26, 45, 61, 74, 79, 150, 153, 198 |
| abstract_inverted_index.five | 168 |
| abstract_inverted_index.from | 82 |
| abstract_inverted_index.huge | 17 |
| abstract_inverted_index.i.e. | 124, 171 |
| abstract_inverted_index.link | 180 |
| abstract_inverted_index.more | 36 |
| abstract_inverted_index.part | 18 |
| abstract_inverted_index.than | 37 |
| abstract_inverted_index.that | 166 |
| abstract_inverted_index.they | 38 |
| abstract_inverted_index.this | 29 |
| abstract_inverted_index.will | 114 |
| abstract_inverted_index.with | 25, 132, 157 |
| abstract_inverted_index.There | 88 |
| abstract_inverted_index.Three | 117 |
| abstract_inverted_index.based | 93 |
| abstract_inverted_index.brand | 42 |
| abstract_inverted_index.fake) | 145 |
| abstract_inverted_index.info, | 101 |
| abstract_inverted_index.media | 100, 105, 108 |
| abstract_inverted_index.owner | 33 |
| abstract_inverted_index.shows | 165 |
| abstract_inverted_index.tags, | 106 |
| abstract_inverted_index.these | 95 |
| abstract_inverted_index.used, | 92 |
| abstract_inverted_index.user, | 151, 154 |
| abstract_inverted_index.users | 80, 188 |
| abstract_inverted_index.Hence, | 11 |
| abstract_inverted_index.Random | 134 |
| abstract_inverted_index.active | 149 |
| abstract_inverted_index.become | 15, 23 |
| abstract_inverted_index.bought | 81 |
| abstract_inverted_index.causes | 30 |
| abstract_inverted_index.class. | 189 |
| abstract_inverted_index.common | 8 |
| abstract_inverted_index.forest | 135 |
| abstract_inverted_index.models | 69 |
| abstract_inverted_index.number | 3, 172 |
| abstract_inverted_index.posts, | 174 |
| abstract_inverted_index.result | 163 |
| abstract_inverted_index.reveal | 194 |
| abstract_inverted_index.should | 39 |
| abstract_inverted_index.users' | 62 |
| abstract_inverted_index.users. | 75, 87, 201 |
| abstract_inverted_index.91.76%. | 161 |
| abstract_inverted_index.becomes | 47 |
| abstract_inverted_index.between | 197 |
| abstract_inverted_index.biggest | 184 |
| abstract_inverted_index.dataset | 77 |
| abstract_inverted_index.highest | 139 |
| abstract_inverted_index.length, | 177 |
| abstract_inverted_index.machine | 67, 111 |
| abstract_inverted_index.market. | 21 |
| abstract_inverted_index.results | 193 |
| abstract_inverted_index.selling | 13 |
| abstract_inverted_index.success | 9 |
| abstract_inverted_index.tested. | 116 |
| abstract_inverted_index.various | 83 |
| abstract_inverted_index.accuracy | 140, 158 |
| abstract_inverted_index.business | 32 |
| abstract_inverted_index.classify | 71 |
| abstract_inverted_index.contains | 78 |
| abstract_inverted_index.features | 91 |
| abstract_inverted_index.identify | 60 |
| abstract_inverted_index.inactive | 152 |
| abstract_inverted_index.learning | 68, 112 |
| abstract_inverted_index.metadata | 169 |
| abstract_inverted_index.produces | 137 |
| abstract_inverted_index.proposes | 65 |
| abstract_inverted_index.research | 57 |
| abstract_inverted_index.services | 14 |
| abstract_inverted_index.sources, | 84 |
| abstract_inverted_index.sources: | 96 |
| abstract_inverted_index.spammer) | 155 |
| abstract_inverted_index.2-classes | 127, 143 |
| abstract_inverted_index.4-classes | 147 |
| abstract_inverted_index.algorithm | 136 |
| abstract_inverted_index.authentic | 72, 86, 200 |
| abstract_inverted_index.behavior, | 63 |
| abstract_inverted_index.biography | 176 |
| abstract_inverted_index.bombarded | 24 |
| abstract_inverted_index.determine | 50 |
| abstract_inverted_index.different | 118 |
| abstract_inverted_index.followers | 5, 12, 27, 46 |
| abstract_inverted_index.important | 48 |
| abstract_inverted_index.metadata, | 98 |
| abstract_inverted_index.metadata. | 133 |
| abstract_inverted_index.proposed, | 123 |
| abstract_inverted_index.4-classes, | 129 |
| abstract_inverted_index.Instagram, | 1 |
| abstract_inverted_index.algorithms | 113 |
| abstract_inverted_index.approaches | 119 |
| abstract_inverted_index.followers, | 175 |
| abstract_inverted_index.following, | 178 |
| abstract_inverted_index.indicator. | 10 |
| abstract_inverted_index.noticeable | 195 |
| abstract_inverted_index.predictors | 185 |
| abstract_inverted_index.statistics | 192 |
| abstract_inverted_index.supervised | 66 |
| abstract_inverted_index.variables, | 170 |
| abstract_inverted_index.(authentic, | 144, 148 |
| abstract_inverted_index.Identifying | 44 |
| abstract_inverted_index.Influencers | 22 |
| abstract_inverted_index.descriptive | 191 |
| abstract_inverted_index.differences | 196 |
| abstract_inverted_index.engagement, | 103 |
| abstract_inverted_index.influencer. | 55 |
| abstract_inverted_index.similarity. | 109 |
| abstract_inverted_index.authenticity | 52 |
| abstract_inverted_index.availability | 181 |
| abstract_inverted_index.endorsement. | 43 |
| abstract_inverted_index.Additionally, | 190 |
| abstract_inverted_index.classification | 121, 125, 131 |
| abstract_inverted_index.classification, | 156 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.97499765 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |