Guest Post: Homophily in TikTok Recommendations
This is a guest post from @lilweehag.
Summary: Earlier this year, Marc Faddoul reported that
TikTok’s “recommended profile” feature seemed to recommend demographically
similar profiles (e.g a user visiting a profile of a bearded white man would be
recommended the profiles of other bearded white men). His results were
suggestive but small-scale. I therefore repeated his research on a larger-scale
set of 7,285 recommendation pairs, with gender and race classified by a machine
learning system. I find that TikTok recommendations are homophilic by gender (p
< 1e-43) and race (p < 1e-79). Consistent with TikTok’s stated
goal of “elevating black creators,” I find that profiles
containing a picture of a black or African-American person are recommended ~60%
more than chance would predict (p < 1e-11).
Background
It is well understood that people connect with others who are
similar to them. This is sometimes referred to as “the homophily principle” and
is described by McPherson
et al. 2001 as:
Similarity
breeds connection. This principle—the homophily principle—structures network
ties of every type, including marriage, friendship, work, advice, support,
information transfer, exchange, comembership, and other types of relationship.
The result is that people's personal networks are homogeneous with regard to
many sociodemographic, behavioral, and intrapersonal characteristics.
There is a wide body of research investigating homophily in social
media, such as on Twitter and Instagram.
TikTok has received relatively little investigation, presumably because of its
newness (although see e.g. Serrano et al. 2020).
Methods
I visited the profiles of the 500 most followed TikTok creators.
For each one, I recorded their username and profile picture, as well as the
usernames and profile pictures of the accounts recommended by TikTok. I used
the clarifai
demographic API to classify each profile by gender
and race. Any classification with a confidence less than 75% was discarded. I
performed additional manual classification of 70 profiles which were frequently
used but the API could not classify.
This left me with 1,506 recommendation pairs where both the source
and recommended profiles had a classified race, and 4,622 pairs with classified
genders.
In this post, the profiles I originally visited (of the 500 most
followed creators) are referred to as “source” profiles, and the profiles
TikTok recommended me to follow are referred to as “recommended” profiles.
Please note that I was not logged into TikTok while performing
this, but TikTok still (presumably) had access to various pieces of information
about me such as my IP address. I believe that this may explain why Asian
profiles were underrepresented in my recommendations – many of the top Asian
creators are Indian, and TikTok’s recommendation algorithm may reasonably have
declined to recommend these profiles to me on account of me living in the US.
Results
Gender
Gender of recommended profile
|
|||
Female
|
Male
|
||
Gender
of source profile
|
Female
|
1188
|
921
|
Male
|
907
|
1606
|
There appears to be homophily in these recommendations. E.g. 56%
of the recommendations from female profiles are female, vs. 36% of the
recommendations from male profiles being female, a difference of 56%. A
chi-square test rejects the null hypothesis at p < 1e-43.
Men make up a similar fraction of both source and recommended
profiles (54.4% versus 54.7%).
Race
Race of recommended profile
|
||||||
Asian
|
Black or African American
|
Hispanic, Latino, or Spanish origin
|
Middle Eastern or North African
|
White
|
||
Race of source profile
|
Asian
|
102
|
38
|
36
|
5
|
54
|
Black or African American
|
7
|
93
|
23
|
2
|
92
|
|
Hispanic, Latino, or Spanish origin
|
13
|
49
|
42
|
2
|
92
|
|
Middle Eastern or North African
|
0
|
0
|
0
|
1
|
5
|
|
White
|
36
|
168
|
117
|
9
|
520
|
(Note: I am using the race categories defined by clarifai; I have
no strong opinion about whether these are the most useful categories.
Homophilic recommendations are the entries along the diagonal and are
italicized for convenience.)
A chi-square test rejects the null hypothesis at p < 1e-79. As
one example: we can see that 43% of the recommended profiles from an Asian
profile are themselves Asian, versus only 4% of the recommendations from a
white profile being Asian.
Unlike with gender, there does seem to be a substantial difference
in the racial representation of source versus recommended profiles:
Race
|
Source #
|
Source %
|
Rec #
|
Rec %
|
Relative change
|
Asian
|
235
|
0.16
|
158
|
0.10
|
0.67
|
Black or African American
|
217
|
0.14
|
348
|
0.23
|
1.60
|
Hispanic, Latino, or Spanish origin
|
198
|
0.13
|
218
|
0.14
|
1.10
|
Middle Eastern or North African
|
6
|
0.00
|
19
|
0.01
|
3.17
|
White
|
850
|
0.56
|
763
|
0.51
|
0.90
|
“Black or African-American” profiles are displayed 60% more as
recommended profiles than as source, and Asian and White profiles are
recommended somewhat less frequently than one would predict from their source
makeup. Again, a chi-square test rejects the null hypothesis at p < 1e-11.
One possible explanation is that TikTok is intentionally promoting
the profiles of black creators. This is consistent with broad marketing claims
made by TikTok, although I am not aware of them ever describing this particular
detail.
Conclusion
Homophily is an important driver of social networks, whose
implications we do not yet fully understand. I hope these results contribute to
our body of knowledge about this phenomenon.
As always, code and raw data can be found
on GitHub. This is a relatively
straightforward experiment, and I encourage others to do their own
investigations. In particular, I would be interested in whether these results
replicate if the source profiles are randomly selected creators, instead of the
most popular creators.
I would like to thank Marc for noticing this phenomenon.
🔥
ReplyDeleteThis is entirely consistent with what I've discovered with my own research. It's nice to see the numbers behind this and to compare and contrast it to my own findings. I do think there's an area of study that you could cover for future blogs. Aligning with this theory, there's a bit of a rift between homosexual and heterosexual tiktok. But, I suspect a lot of that algorithm is still muddied, since many people watch the same gender for comparison reasons, not sexual ones. I have found that the algorithm tends to confuse comparison with sexual attraction and probably overly conflates the number of homosexual/bisexual/whatever.
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