# Characterising and Detecting Sponsored Influencer Posts on Instagram

Koosha Zarei1, Damilola Ibosiola2, Reza Farahbakhsh1, Zafar Gilani2, Kiran
Garimella3, Noël Crespi1, Gareth Tyson2 2Queen Mary University of London,
United Kingdom. {d.i.ibosiola, z.gilani<EMAIL_ADDRESS>
3Massachusetts Institute of Technology<EMAIL_ADDRESS>1Institut
Polytechnique de Paris, Télécom SudParisFrance. {koosha.zarei,
reza.farahbakhsh<EMAIL_ADDRESS>
###### Abstract

Recent years have seen a new form of advertisement campaigns emerge: those
involving so-called social media _influencers_. These influencers accept money
in return for promoting products via their social media feeds. Although this
constitutes a new and interesting form of marketing, it also raises many
questions, particularly related to transparency and regulation. For example,
it can sometimes be unclear which accounts are officially influencers, or what
even constitutes an influencer/advert. This is important in order to establish
the integrity of influencers and to ensure compliance with advertisement
regulation. We gather a large-scale Instagram dataset covering thousands of
accounts advertising products, and create a categorisation based on the number
of users they reach. We then provide a detailed analysis of the types of
products being advertised by these accounts, their potential reach, and the
engagement they receive from their followers. Based on our findings, we train
machine learning models to distinguish sponsored content from non-sponsored,
and identify cases where people are generating sponsored posts without
officially labelling them. Our findings provide a first step towards
understanding the under-studied space of online influencers that could be
useful for researchers, marketers and policymakers.

###### Index Terms:

Influencers, Instagram, Social Media, User Behaviour Analysis, Sponsored
Content

##  I Introduction

Social media has become an active part of billions of people’s lives.
Initially intended as a method to interact amongst friends, it has since
become a platform used heavily for marketing. This is dominated by players
such as Instagram who allow third parties to micro-target adverts at users
matching certain criteria. This newfound ability has turned social media into
a multi-billion dollar industry. However, in recent years a new breed of
social advertisement has emerged. Driven by the rise of social media
celebrities, so-called _influencers_ , we have witnessed various companies
marketing directly through celebrity endorsements (rather than via official
advertisement channels on the platform). For example, this may involve a
celebrity posting an image of them wearing a particular item of clothing or
driving a particular car. Whereas endorsement by celebrities is nothing new,
social platforms have enabled this to take place on an industrial scale. The
proliferation of these activities, via the use of Influencer
Markets,111https://influencermarketinghub.com/influencer-
marketing-2019-benchmark-report/ has also dramatically increased the scale and
accessibility of these types of promotion.

A particularly interesting trend is the rise of _nano_ influencers [1]. These
users may only have a small number of followers, but often have reach into
highly targeted audiences. This has radically increased the number of accounts
actively promoting products, thereby making the industry challenging to
regulate. This is because such social media posts are highly opaque, often
making it impossible to identify the difference between paid promotions, and
genuine personal endorsements. This distinction is important, as personal
endorsements do not fall under the same stringent regulatory framework as paid
sponsorship. To address this, standards bodies such as the UK’s Advertising
Standards Authority (ASA) have created a set of rules asking that such post
are explicitly tagged with relevant hashtags (_e.g._ #ad) [2]. Similarly
platforms such as Instagram, have allowed such information to be formally
embedded within posts via their user interface. To date, however, the research
community has limited knowledge of how these practices are employed, or even
how influencer marketing takes place more generally.

This paper strives to fill this gap, and makes two contributions. _First_ , we
present a broad characterisation of influencers on Instagram, and formally
quantify their behavioural attributes. Although inherently of value, we also
argue that this has a number of implications for emerging regulation in this
sector. To this end, we collect a large-scale Instagram dataset consisting of
12k influencers (§III). We gather both Instagram posts and stories222Stories
are time limited posts that automatically delete after 24 hours. from users
who attach advert-related hashtags to their material.

With this dataset, we proceed to explore influencer characteristics (§IV). We
see a range of promotion activity taking place, ranging from “mega”
influencers with over 1 million followers to “nano” influencers with as little
as 500 followers. We find a range of products being produced with health &
beauty taking the top spot, followed by services, clothing and food featuring
prominently. Naturally, top mega influencers garner the greatest attention, as
measured via likes and comments. Yet we find that small “nano” influencers
sustain attention more effectively. For example, we find that a far larger
fraction of nano influencers’ followers engage with posts, thus forming a
denser social network (whereas, in absolute terms, higher popularity
celebrities gain more likes). This suggests that these different types of
influences offer marketers very different styles of promotion. We also observe
cases where users are creating sponsored posts, yet failing to attach
appropriate labels to inform audiences that they are receiving payment. This
not only has the potential to harm vulnerable users (_e.g._ children) but also
raises legal questions.

This drives us to make our _second_ contribution (§V), where we strive to
build automated tools to detect sponsored posts that fail to explicitly tag
themselves. We propose a Contextual LSTM Neural Network classifier which
considers the text content and other metadata to identify whether a post is
sponsored or not. We obtain an accuracy of 89%, and we identify a set of
prominent predictive features, _e.g._ use of language, the inclusion of URLs
and commentary. Our findings have important implications for social media
companies, marketers, researchers, and regulators wishing to better understand
the behaviour of influencers.

##  II Background & Related Work

The concept of social influence is a well studied topic. It has long been
known that an elite of _influencers_ can have a disproportionate impact on
social structures [3, 4]. This has subsequently led to the rise of
_influencers_ , who monetise their influence via sponsorship payments. The
premise is that, via electronic word of mouth, brands can be promoted through
influential endorsements, akin to traditional sponsorship deals [5]. More
formally, sponsored influencers are “third party individuals who significantly
shape the customer’s purchasing decision, but may never be accountable for it”
[6].

There have been a number of recent studies looking at influencer marketing,
primarily within the business research community. These have followed a number
of methodologies, including interviews with marketers [7, 8] and customers
[9], as well as qualitative studies of specific influencers [10]. There have
been further preliminary studies of the perception of influencers that, for
example, found people to view influencers as smart and ambitious [11, 12].

There have also been a small set of studies that rely on computational
datasets. Lahuerta _et al._ [13] studied the role of marketing influencers in
the Japanese automotive sector. They collected 30K tweets based on relevant
keywords. They found, amongst other things, that the sentiment and length of
influencer tweets has a positive effect on popularity. Although highly
relevant to our work, it is unclear how these trends generalise across other
brands and large-scale influencer networks. Closest to our work is [14], which
explored the follower graphs of influencers on Instagram. They targeted 218
specific social influencers (with 8.9M followers) as registered via Popular
Pays, a platform used to connecting brands to influencers. These were then
compared against 948 Instagram users with fewer than 10K followers. They
studied the social graph properties of these users, to find that influencers
have large numbers of followers, and even tend to interconnect amongst
themselves. They also found that users often follow multiple influencers.
Although highly applicable, we argue that these targeted studies likely only
capture a small slice of the overall influencer market. They have a particular
bias towards certain types of highly popular influencers, and ignore a
potentially larger population of micro influencers. To the best of our
knowledge, this is the first large-scale study of influencer activities.

##  III Data Collection Methodology

We start by explaining our data collection methodology. Our data collection
covers both Instagram posts and the recently introduced stories feature.

###  III-A Data Collection

Our data collection activities took place over four phases:

Phase 1: Hashtags. It is first necessary to obtain a large list of influencer
accounts. One approach would be to manually curate this set, however, this
would limit us to a small set of influencers, largely dominated by well known
celebrities who are easy to identify. Hence, we compile the list by crawling
all posts attached to a set of influencer-related hashtags. We turn to the
UK’s Advertising Standards Agency [2], which states that influencers should
use the #ad, #advert or #sponsored hashtags in _any_ posts that have been paid
for. We expand this list with #advertising, #giveaway, #spon and #sponsor
[15].

Phase 2: Post & Stories Collection. We then use the official Instagram API
[16] to gather all posts and stories that include any of the above hashtags.
Note that stories are similar to normal posts, yet they are automatically
deleted after 24 hours (akin to Snapchat posts). Hashtag Engine is used [17]
with a maximum of 30 unique hashtags. This API returns public photos and
videos that have been tagged with specific hashtags. Our crawl for posts and
stories ran between Sep 2018 and April 2019. This process identifies 12K
accounts that have posted using the previously mentioned hashtag.

Phase 3: Account Collection. Although the above yields a substantial body of
posts and stories, we are primarily interested in gathering data on a per-
influencer basis. Hence, we next extract all accounts identified from the
Phase 2 dataset and begin dedicated monitoring for all posts and stories
generated by those users. (_i.e.,_ influencers). This covers all posts,
reactions and stories from those accounts from July 2019 to August 2019. In
this step, we collect 19.7K posts, 63K stories, 3.1M comments, and 27M likes
(generated by the 12K user accounts from Phase 2). Note that they contain a
mix of both sponsored (16%) and non-sponsored (84%) entities. For each post,
we collect the image, comments, likes and public profile information of the
user, as well as any other users who reacted to the post. For each story, we
collect the equivalent information, although we cannot collect likes (as these
are not available in stories). Each sponsored post is also tagged with the
product being advertised, and the category of advertiser. In total, we have
35K posts, 99K stories, 3.1M comments, and 27M likes generated by 12K users.

Phase 4: Categorization. Once we have collected the posts and stories, it is
necessary to tag explicitly which are considered sponsored. We take a simple
approach. If a post is tagged with one of the above hashtags, we assume that
it is sponsored. In the case of Instagram stories, there is explicit metadata
which tells us if it is sponsored (the Paid Partnership tag). Hence, for
stories we rely on this metadata item (rather than hashtags). Note that this
excludes posts that are sponsored, yet the user does not add the appropriate
hashtag. We revisit this issue in §V.

Ethics. In line with Instagram policies as well as user privacy, we only
gather publicly available data that is obtainable from Instagram. Whereas we
do analyse the content of influencer posts, we do not inspect the content of
comments submitted by non-influencer users.

###  III-B Data Validation

A natural risk is that a subset of the posts containing the curated hashtags
may be generated by users who are not influencers. Although it is impossible
to entirely discount this at scale, we further perform manual annotation to
validate the general correctness of our data. To validate our dataset, we
manually looked at the profiles of the influencers to verify if they were
really promoting sponsored content.

All users with above 10K followers are checked, confirming that they were all
correctly tagged as posting sponsored content. We further check 25% (2K) of
all influencers with under 10K followers. We find that we the above approach
yields 97.6% accuracy: just 48 accounts were incorrectly classified as
influencers. Note that the above only checks if a user account has one more
truly sponsored posts. To provide further confidence we randomly select 500
influencers and check _all_ of their posts. Around 80% of sponsored-post are
correctly classified as the sponsored content (based on the hashtags
previously mentioned). We filter any incorrectly identified influencers.

##  IV Characterizing Influencers

We begin by exploring influencers’ follower counts and engagement levels
(comments and likes), before profiling the types of products promoted.

(a) All dataset (b) Follower per account

Figure 1: Follower counts: (a) CDF of followers and followees of all the
influencers in our data. (b) CDF of number of followers per account separated
in groups.

###  IV-A How popular are influencers?

We take inspiration from past work [18], and begin our analysis of influence
by looking at follower counts. This is a natural indicator of influence as it
captures the upper-bound of people to whom posts are directly pushed. Figure
1(a) presents the cumulative distribution function (CDF) of the follower and
followee counts of the influencers in our dataset.

Unsurprisingly, we see a sizeable fraction of extremely popular accounts. 35%
of users have over 100K followers, and 17% posses over 1M. These conform to
the common interpretation of influencers. More surprising, however, is the
presence of a long tail: 37% of accounts have fewer than 10K influencers, with
15.5% even having below 1K. At first, we suspected that this may be caused by
miscellaneous use of the advert-related hashtags. However, upon manual
inspection, we confirm that these are indeed influencers. For instance,
@lesya_9_9 with 500 followers has promoted over 10 times more regularly than
@blogging_with_tiffany who has fewer than 100 followers. This reveals a
growing set of small-scale influencers who promote products, despite their low
follower counts. In other words, _influencers are not just celebrities_ : they
appear to encompass a morass of different account types. For context, we can
contrast these results with the followee count (Figure 1(a)) which are, on
average, far lower than follower numbers. Whereas the median follower count is
23.7K, it is just 770 for the number of followees.

TABLE I: Influencer Profile Characteristics

| Influencer
---
Category
| #Followers
---
Category
| Avg.
---
follower
| Avg.
---
followee
| Avg.
---
mediacount
| % of
---
verified
Mega | $\geq 1M$ | 5.8m | 845 | 9.1k | 82%
Macro |  $<1M$ & $\geq 100K$ | 257k | 1.3k | 3.1k | 22%
Micro |  $<100k$ & $>10K$ | 32k | 1.9k | 1.8k | 4%
Nano | $<10K$ | 1.5k | 0.9k | 597 | 0.5%
The key profile characteristics of influencers (full timeline). |

Based on the above findings, we categorise influencers into 4 distinct
categories based on their reach (# followers). This taxonomy underpins our
subsequent analysis. We term these nano, micro, macro and mega influencers.
Table I presents a summary of these groups. We note that 80% of mega
influencers are verified by Instagram, but in contrast, under 5% of nano and
micro accounts have a blue verified icon.333Users on Instagram can get
verified badge as low as 500 followers. However, that account must represent a
well-known, highly searched for person, brand or entity [19] Figure 1(b)
presents the CDF of follower counts per-account, broken into these four
groups. Naturally, the distributions reflect the split with nano influencers
having the fewest followers. For context, a few examples of influencers (top
three in terms of followers) from the each categories is shown in Table II. As
previously discussed, primarily the more “popular” influencers have verified
accounts. These users tend to also have more posts on average.

TABLE II: Examples of influencers

Category | Username | Section | #post | #follower | #followee | #verified | #url
---|---|---|---|---|---|---|---
Mega | @kendalljenner | Fashion | 3K | 116M | 203 | ✓ | -
@vanessahudgens | Fashion | 3.2K | 36M | 1.1K | ✓ | -
@brentrivera | Lifestyle | 1.8K | 16M | 395 | ✓ | Youtube
Macro | @tonyamichelle26 | Lifestyle | 5.3K | 937K | 2.1K | - | Business Page
@alice_gao | Design | 4.1K | 910K | 500 | ✓ | Business Page
@lilleejean | Beauty | 700 | 950K | 650 | ✓ | Youtube
Micro | @charisseo_ | Fashion | 1.7K | 98K | 600 | - | Email
@morgbullard | Lifestyle | 1.9K | 97.5K | 1.3K | - | Business Page
@ginascrocca | Fashion | 246 | 96K | 1K | - | Business Page
Nano | @jaimesays | Travel | 600 | 9K | 1.9K | - | Business Page
@aberhalloooo | Food | 1.1K | 9K | 7K | - | Email
@lawrence.carlyFollow | Dance | 393 | 8.1K | 1.2K | - | Business Page

###  IV-B Do followers engage with influencers?

Another way to measure “influence” is to inspect engagement levels on a users’
posts, _e.g._ comments, likes and mentions. Previous studies [18] have argued
that these levels can be a better proxy for influencer than simply inspecting
follower counts. In this section, we directly contrast engagement levels for
posts that are sponsored _vs._ not sponsored.

Active attention - Comments. Figure 2(a) presents the CDF of the received
comments per-post for each influencer. We separate posts into sponsored and
non-sponsored posts within the influencer timelines. In almost all the cases
we observe that sponsored posts receive fewer comments from the users. This
suggests that these sponsored posts are of less interest than other posts.
This difference is even more significant in mega influencers, where sponsored
posts gained 10 times fewer comments than their non-sponsored counterparts. We
also observe that the number of comments are ranked in order of Mega, Macro,
Micro and Nano influencers with, unsurprisingly, Mega influencers getting over
40 times more comments than Nano.

The above analysis of absolute counts may give a misleading perspective as
influencers with high follower counts (_e.g._ Mega) will obviously obtain
higher comment counts. Hence, we normalize the comment count as a fraction of
the follower count, and plot the results in Figure 2(b). Here, we see rather
different trends with nano influencers gaining the most engagement. In other
words, even though popular accounts gain more comments, less popular accounts
obtain engagement from a higher fraction of their follower-base. This perhaps
sheds light on why nano-influencers have recently started to gain traction,
with their ability to engage more targeted populations.

We also inspect the duration before a user adds a comment on a post.
Intuitively, comments that are issued shortly after a post is created might be
from more engaged users. This confirms similar results to Figure 2, with Nano
influencers gaining posts most rapidly than their more popular counterparts.
In all the cases, non-sponsored posts gain comments earlier; the most
significant difference is for Mega influencers. The median comment age for a
Mega influencer’s non-sponsored posts is 328 _vs._ 366.5 for their sponsored
posts. That said, we observe a subtle difference between the influencer
groups. In the first hour $<$30% of comments of all influencers are issued.
After the first 10 hours, non-sponsored posts of Macro influencers receive
$<$70% of their total comments, but sponsored posts of mega influencers get
only less than 50%. The density of users who reacted to non-sponsored posts is
larger, and this difference is more significant in mega influencers and less
for nano influencers (plot not shown due to space constraints).

Briefly, we also note that nano influencers tend to have more consistent
engagement. Whereas more popular influencers, gain comments from many
different users, nano influencers tend to get comments from the same users
multiple times: 30% give more than one comment (Figure 3b).

(a) Absolute (b) Normalized by followers

Figure 2: CDF of number of comments received per-post: (a) absolute number;
(b) normalized.

Passive attention - Likes . As comments are generally the most active form of
engagement, we also inspect more passive engagement via likes. Figure 3a
presents the CDF of the received likes for both sponsored and non-sponsored
posts among each influencer sategory. For Mega influencers, we observe 28%
more likes for non-sponsored _vs._ sponsored posts. This, however, is far less
for the other categories (macro, micro, and nano), with an equivalent value of
6%. In fact, we observe that sponsored posts gain marginally more likes than
non-sponsored posts for nano influencers (median 56 _vs._ 47). That said, the
categories exhibit broadly similar patterns to that seen in comments (with
Mega gaining the most and nano gaining the fewest likes in absolute terms).
Turning our attention to the normalized like count, we see that again nano
influencers get more likes than the other categories. However, we do see
outliers: for instance, inside nano, nearly 8% of accounts receive a large
number of likes, yet nearly no comments (all posts). To investigate this, we
manually inspect these subsets of nano influencers. We find that there is a
prevalence of fake profiles, apparently using bots to boost their impact.

(a) Passive attention (like) (b) Active attention (comment)

Figure 3: (a) attracted attention CDF of number of likes received per-
influencer; (b) number of comments performed by each user per-influencer

###  IV-C How often do influencers post?

We start by measuring the number of posts per influencer. Figure 4(a) presents
a CDF plot of the number of sponsored _vs._ non-sponsored posts, whereas
Figure 4(b) repeats the same for stories.

(a) Post (b) Story

Figure 4: CDF of the number of posts and stories published per influencer.

We observe distinct distributions, with most influencers publishing more non-
sponsored posts. Only 8.3% of influencers distribute more sponsored posts
compared to non-sponsored. On average, 16% of posts are sponsored with just
9.3% of influencers tagging over half of their posts as sponsored. This is
anticipated as most influencer guides recommend that users keep the percentage
of sponsored posts below 60%, to maintain audience engagement.

Subtle differences can also be observed between the different categories of
influencer. For example, where the mega influencers on average post the most
sponsored posts, they actually post the least non-sponsored posts. Of course,
this might also be a product of how such influencers tag their posts. We find
that no sponsored story is published by any nano influencers. This contrasts
to what is observed with posts from influencers within the same nano category
in Figure 4(a), where over 80% of these influencers publish $\leq$10 posts.
This striking difference suggests that nano influencers are primarily using
posts (rather than stories) to publish sponsored contents. In contrast, mega
influencers tend to use stories to promote sponsored contents more regularly
(compared to macro and micro influencers). For example, $\leq$21% of Macro and
$\leq$3% of micro influencers publish more than 10 sponsored stories compared
to over 30% for mega publishers. In general, we see that influencers across
mega, macro and micro category favor the use of stories to promote sponsored
content compare to post, possibly because it is cheaper to advertise via
stories compared to feeds [20]. Another reason for using stories is the
exclusivity _i.e.,_ followers must stay engaged and must hurry to see the
offer or discount code _etc._ while the story lasts.

Figure 5: CDF of number of products promoted by influencers across categories.
This only covers stories because equivalent metadata is not available for
posts.

###  IV-D What do influencers promote?

Finally, we wish to inspect the types of products being promoted by
influencers. This can be done via our Instagram stories dataset, as each
sponsored item is optionally tagged with the category of the advertiser. This
is taken from a control set of tags offered by Instagram. Note that this is
_only_ available for stories, and not regular posts. We find that this feature
is not widely used by influencers, with only 3% stating their product.

Figure 5 presents a CDF showing the number of products promoted by influencers
across categories. Most influencers only promote a single product,
particularly in the case of micro. We observe that 50% _Mega_ , 58% _Macro_
and 70% _Micro_ influencers promote just a single product. This suggests that
influencers tend to focus on a particular product type, likely in their own
specialist area.

We also inspect the type of products influencers use stories to promote. This
metadata is captured by the Instagram API, although it only covers stories, as
posts do not contain this explicit metadata. Figure 6 presents the top 20
product types influencers promote. The Y-axis counts the number of unique
accounts promoting each type of product. We observe that most publishers tend
to advertise products under _Health/Beauty_ (14%), _Product/Service_ (11%) and
_Clothing (Brand)_ (11%). Across products we observe that Mega influencers
tend to dominate: they are the major publishers for 77% of product types we
identify. This is in sharp contrast to Macro (14%) and for Micro (9%). These
findings confirm the intuition that Instagram is dominated by promotions
surrounding consumables such as food, retail and beauty. These cover 43% of
all adverts.

Figure 6: Number of products promoted in stories based on their type
(identified via the Instagram API).

##  V Predicting Sponsored Content

The previous results have shown that sponsored posts tend to have unique
properties that differentiate them from non-sponsored content. This leads us
to inspect the viability of automatically detecting (and tagging such posts).
This, for example, could be used to protect users from being misled.

###  V-A Classifier Design

We take a supervised deep learning approach, as we have a substantial ground-
truth (annotated) dataset of sponsored _vs._ non-sponsored posts.

Dataset. For classification, we use the post dataset described in Section
III-A. In total, the dataset consists of nearly 7K sponsored and 27K non-
sponsored posts from all groups of influencers. Note that we do not
differentiate between the different popularity groups and, instead, treat all
accounts equally. As we are dealing with an imbalanced dataset, we use Random
Under-Sampling to reduce the size of the non-sponsored class. Accordingly, we
randomly select posts from non-sponsored class and remove them from the
training dataset. While selecting the posts, we try to keep the diversity of
influencer’s profiles. So, we select random samples from influencers who have
more published post. This process started from nano (with a higher number of
post) to mega.

Finally, we produce a dataset with 14k observations with an equal number of
sponsored and non-sponsored labelled posts (Section III-A). Note that all
these posts are generated by users who have posted at least one sponsored
post. Hence, our classification task is limited to differentiating between
sponsored and non-sponsored posts from influencers (rather than a more general
audience). Based on our observations, the posts in the above dataset can be
separated into three sup-populations:

  1. 1.

Sponsored Post. In a sponsored post, influencers normally try to directly or
indirectly advertise a product. By adding sponsored metadata (Section III-A),
the post is explicitly declared as an advertisement. It may also contain
related hashtags for that product and the page of that product. We note that
the main company is often tagged in the post.

  2. 2.

Non-Sponsored Post. In contrast to a Sponsored Post, this is a routine post by
an influencer which does not include any direct or indirect advertisements. As
a result, the photo/video, caption, and list of hashtags are not organized or
written in such a way to promote anything.

  3. 3.

Hidden Sponsored Post. This sub-population is similar to the first item
(Sponsored Post) except that sponsored metadata (_i.e.,_ hashtags) are
removed. We remind the reader that The Advertising Standards Authority (ASA)
[21] advises the use of such hashtags, as any paid promotions are subject to
consumer protection law. Despite this, we posit that there is an incentive for
influencers to avoid disclosure, as there is greater value in “personal”
endorsements. “Hidden Sponsored Post”, “Hidden Advertisement” and ”Non-
declared Sponsored Post” terms address the same meaning in this paper.

Figure 7: An Example of non-declared sponsored post: 1) A verified Mega
influencer holding a box on her hand and promoting products. 2) The product
page is tagged in the photo and mentioned in the caption. 3) The caption
invites us to use this product, but there is no sign of sponsored metadata. 4)
The post received 4K likes and 500 comments.

Feature Engineering & Pre-processing. The next step is to build a set of
features from the post content to train our model. Table III summarizes the
features that are employed. We split the feature list into two main
categories: post features and publisher features. Post features comprise all
features that are obtained from the content of the post, _e.g._ number of
likes, the caption. Publisher features cover features extracted from the
profile of the publisher. For all text-based features such as “post caption”
and “post hashtag”, we remove all punctuation marks, stopwords and convert
them to lowercase characters. Words are stemmed to reduce to their root forms.

TABLE III: Feature Set Used for Classification.

Post Features | Publisher Features
---|---
Feature | Type | Feature | Type
post caption | text | number of follower | numeric
post hashtag | text | number of followee | numeric
number of likes | numeric | length of biography | numeric
number of comments | numeric | profile biography | text
length of caption | numeric | is verified | numeric
number of hashtags | numeric | external URL exist | numeric
number of mentions | numeric |  |
number of tagged users | numeric |  |

Model Architecture & Performance. Next, we propose a Contextual LSTM Neural
Network architecture (presented in Figure 8) and we compare the result with
Random Forest as a classic machine learning based Classifier. Post metadata
can enrich the information available for classification. So, to build the
model, we combine texts with other metadata features (Table III). First, we
tokenize text metrics (_e.g._ caption and hashtag) using Keras Tokenizer Class
[22] and then, the result is fed to the LSTM layer which outputs a
64-dimension vector. Then, we attach numerical metadata (post and profile
features) to this vector and pass it through 2 ReLU activated layers of size
128 and 64. Finally, it connects to an output layer that predicts the label.

Figure 8: General architecture of sponsored content detection (Contextual LSTM
model).

We use TensorFlow, Keras, and Scikit-learn libraries, and we run them on
Google CoLab. Also, we use a random split of 80% (training set) and 20% (test
set) and to avoid overfitting, we use 10-fold Cross-validation. For each
model, we compute the accuracy, precision, recall and F1 in Table IV. For our
labelled data, we observe high performance, with positive results across both
classifiers. Our Contextual LSTM Classifier enhances the performance by nearly
5% (89% of accuracy).

TABLE IV: Classifier Performance

Model | Accuracy | Precision | Recall | F1
---|---|---|---|---
Random Forest | 0.84 | 0.83 | 0.84 | 0.83
Contextual LSTM | 0.89 | 0.88 | 0.87 | 0.89

###  V-B Results

We now evaluate our classifier across the entire dataset to quantify the
prevalence of hidden sponsorship.

Detection. As we believe some influencers do not declare sponsored posts with
defined metadata (§III-A), we use the DNN trained model on the non-sponsored
posts which are not used in the training. This part includes nearly 20K posts
from all 4 groups of influencers (mega to nano).

Figure 9: Overall view of different post distributed by influencers.

Results. Our model detects that 17.7% sponsored posts are published without
metadata (or not declared as sponsored) which is approximately 3.8K posts
through all influencers. Figure 9 demonstrates the portion of non-sponsored,
sponsored and non-declared sponsored post across each group. We see a
noticeable set of hidden sponsored posts, particularly for Micro and Nano
influencers. Nano with 18%, micro with 21%, and macro with 11% of shared
posts, advertise almost 4 times more than Mega. In detail, by considering
clusters of influencers we observe:

  * •

Mega. Mega is the smallest group in terms of having hidden advertisements.
Nearly 29% of Mega influencers publish one non-declared post. In detail, out
of 3K non-sponsored posts, 4.3% are detected as hidden advertisement entities.

  * •

Macro. 57% of Macro influencers have one hidden sponsored post. In detail, 13%
of 3.6K non-sponsored posts are recognised as hidden advertisements.

  * •

Micro. Micro is the largest group in terms of publishing non-declared
advertisements. In general, 78% of Micro influencers publish one sponsored
post. In detail, 24% of the 5K non-sponsored content is detected as hidden
advertisements.

  * •

Nano. More than 70% of nano influencers publish one non-declared
advertisement. In detail, within 9.7K non-sponsored posts, 20% are detected as
hidden advertisements.

Validation. To validate the above results, we manually validate 50% of the
detected posts to confirm they are hidden sponsored posts. Across the
identified content, we witness in 81% of posts, influencers are confirmed as
promoting products, 11% are incorrect, and in 8% we are not able to verify
they are commercial or not. This confirms that our model _is_ effective at
identifying influencer posts.

###  V-C Text Feature Analysis

Finally, we manually explore which textual features (Table III) are most
prominent in our prediction task. We do this to gain an understanding of what
characteristics are most common specifically for (hidden) sponsored posts.
Thus, we manually check 50% of the predicted posts and for each metric, we
inspect the unique distinctions between sponsored and non-sponsored posts.
This allows us to compile a list of differences and then count how often these
difference emerge. We summarise our findings as follows:

  1. 1.

Post Caption holds the most valuable information of the post in the shape of
the text (Fig 7). Often the text includes details of products, promotions,
names, _etc._ and encourages users to perform an action. An action could be to
buy something, follow a page, join a competition, install an application, use
a specified hashtag, _etc._ We also notice some peculiar way of writing the
text or using keywords as follows. If X represents a product or producer: (i)
the caption includes X as raw text in 94% of the sponsored posts. (ii) Also X
is mentioned (followed by @ sign and becomes blue) in 91% of the posts. (iii)
The caption could contain particular sentences such as “thank you X”, “many
thanks to X”, “X from this page”, “my top choice is X”, “go and follow X”,
_etc._. This happens in 78% of the sponsored posts. (iv) We observe call-to-
action keywords such as “link in bio”, “download it”, “watch my story”, “Use
discount code” and “comment/like to win”, _etc._ in 53% of the posts.

  2. 2.

Post Hashtag is the next leading text feature which generally includes
valuable information about the product and its producer. Influencers may use
one or more hashtags for the corresponding product (Figure 7). For example, if
#outfit is the main hashtag representing the product, we also observe
#outfitday, #oufit, #bestoutfit, #trendyoutfit, #outfitstyle, _etc._ In 97% of
the sponsored posts, the name of the product or producer (or both) is listed
as a hashtag. To highlight some hashtags, influencers may put them in the text
while describing the products, or create a separate list after the text (or in
some cases in the first comment by the influencer). Hashtag count also helps
differentiate sponsored content. Sponsored posts one to get hashtags trending,
or (ii) to get more visibility in Instagram explorer, or (iii) to be easily
found in search results, regularly have longer hashtag length:

  3. 3.

Profile Biography is another feature that improves the accuracy of our
classifier. In general, influencers usually put relevant information in their
profile biography (which could be temporary): (i) In 63% of the biographies,
information such as “sponsor info”, “campaign details” or “promotion code” as
raw text exist. (ii) In 54% of the profiles, influencers put sponsor
“hashtag(s)” (which becomes blue) or mention “product/producer page” (followed
by @ sign). (iii) In 34% of the profiles, there is a call-to-action phrase
with special keywords such as ”follow”, “buy”, “sale”, “watch”, “join”, “check
out”, “promotion”, “more info” _etc._ (iv) Profiles may include an External
URL (in 21% of the profiles) or YouTube link (in 11% of the profiles), which
redirects users to the main product webpage, producer website, or full youtube
review video. Regularly influencers use shortener tools to monitor the number
of people clicking on it. Biography length is also helpful as influencers who
do promotion usually have (i) promotion codes, (ii) sponsor contact detail,
and (iii) product-related hashtags on their biography (longer biography).

##  VI Conclusion & Future Work

This paper has performed the first large-scale analysis of influencer
behaviour on Instagram. Rather than solely discovering “celebrities”, our
methodology has exposed a large array of influencer types, including highly
targeted nano influencers. We have further dissected the differing behaviours
of these influencers, to find that even though Mega influencers garner
significant attention, nano influencers are more successful in engaging their
audiences with a larger fraction of followers interacting with their posts. We
have further trained a DNN model to classify posts as sponsored, allowing us
to identify seemingly sponsored posts that are not properly declared. As
influencer income is taxable, we note that this may have financial
ramifications that go beyond the issues of deceiving consumers.

This is just the first step in a wider research agenda. We have several
specific lines of future work. First, we wish to expand our analysis across
multiple platforms (_e.g._ Tik Tok, YouTube) and to gain a deeper
understanding of the strategies employed by influencers. Second, we wish to
revisit our classifier to improve performance. Although our current
implementation obtains good results, we manually found 11% misclassifications.
Consequently, we wish to build on our work to devise classifiers that can
automatically identify influencers. This is particularly important for
identifying “ghost” influencers who do not explicitly identify their adverts.
We posit that this could also be used to support social media giants in
identifying misbehaviour within the advertisement space. Whereas in this
paper, we have focused on influencers themselves, we are also curious to
better understand the dynamics of _audiences_ as well as the _companies_ that
use influencers.

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