Analysis of Influencing Factors on the Number of Likes on TikTok

Ruibo Huang

Guangzhou Xiangjiang Zhongxue, Guangdong, 510000, China

Keywords: TikTok Likes, Regression Analysis, Video Type, Video Length, Second-Person Video.

Abstract: Given the speed at which technology is developing and the growing acceptance of the Internet, the status of

social media has steadily risen, and short-video platforms led by TikTok have captured the attention of the

majority of the public. The number of likes on TikTok is one of the key research topics today, and analyzing

the influencing factors behind it has become crucial. The data in this article is sourced from the TikTok

accounts of 11 of the most followed news agencies in the United States and Europe, and based on this, some

tags with higher views have been classified. This article mainly conducts a multiple linear regression analysis

on the data to investigate the primary determinants of TikTok's like count. Research finds that the type of

video significantly affects the number of likes; videos in the second-person perspective are closely associated

with the quantity of likes, and the length of time since the video was posted is positively correlated. However,

as the number of likes is influenced by multiple factors, there may be omissions in the summary analysis, and

further in-depth research is needed in the future.

1 INTRODUCTION

New social media platforms continue to appear as a

result of changes in internet user tastes and

information technology advancements. Short-video

platforms, with their characteristics of short duration,

rich content, and diverse forms, have rapidly spread

on the internet and attracted a large number of users.

Among them, TikTok, with its huge user activity

volume, has become one of the mainstream media. It

was the mobile application with the largest download

volume in 2018 and 2019 (Li et al., 2021), and it is

also the most widely used social networking site

among Chinese Millennials (Qin, Omar and Musetti,

2022). Moreover, statistics show that the most active

group on Tiktok are younger users (Montag et al.,

2021). However, unexpectedly, the uninstallation rate

of TikTok is 9.43% (Rahimullah et al., 2022), which

is far greater than that of other social networking sites.

The most efficient method of improving user

communication on TikTok is to look at the number of

likes, which is considered a significant interaction

indicator. The number of likes directly reflects users'

preferences and behavioral patterns, and studying its

influencing factors can better understand users'

https://orcid.org/0009-0000-9778-0258

consumption psychology and behavioral

characteristics. Irfan and Yaqoob (2024) pointed out

that the algorithm of Tikotok drives the dissemination

of content, allowing information to spread rapidly and

reach diverse and extensive audiences. At the same

time, the recommendation mechanism of TikTok

determines that videos with high numbers of likes are

more likely to be recommended (Sun, Zheng and Wu,

2023). Therefore, analyzing the influencing factors of

the number of likes on short videos is of great

significance for formulating more reasonable

operation strategies, promoting more efficient big

data algorithms for recommendation, and enhancing

user stickiness.

At present, international scholars have conducted

extensive research on the TikTok platform. For

example, Sharabati et al. (2022) discovered that the

desire for fame and wealth, social recognition, and

self-expression are the main reasons people use

TikTok. Shutsko (2020) stated that TikTok videos are

for entertainment purposes and combine self-

expression and self-presentation. These studies have

a wide exploration range, but there are certain

limitations in terms of specificity and purposefulness,

making it difficult to fully reflect the specific

626

Huang, R.

Analysis of Inﬂuencing Factors on the Number of Likes on TikTok.

DOI: 10.5220/0013834600004708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 626-630

ISBN: 978-989-758-774-0

situations in a certain country or globally. In contrast,

from a micro-level perspective, analyzing the

influencing factors of the number of likes helps to

reveal the dissemination mechanism of TikTok and its

key driving factors.

This study is based on the regression analysis

method and aims to analyze the multiple influencing

factors of the number of likes on TikTok, close the

current research gap, assist in raising Douyin's user

retention rate, and encourage the creation and sharing

of high-caliber content, and thereby promote the

healthy development of online culture.

2 METHODOLOGY

2.1 Data Source and Description

The data of this article is sourced from 1000 videos

on the TikTok platform. The selection of these videos

is based on their popularity, including the number of

shares, comments, and likes. A more in-depth

analysis was conducted on 100 of the most popular

videos, and the popular types of videos and their

influencing factors were discussed. The likes, shares,

and comments of these videos accounted for the vast

majority of all videos, namely 63.25%, 81.37%, and

76.73%, respectively. The data mainly comes from

Germany, as the technical requirements of TikTok

limit the sources of videos, which causes some

limitations in the research results. For example,

cultural factors may affect users' content preferences.

In addition, this article also collected the analysis

data of videos posted by 11 major news agencies

based in the United States and Europe on TikTok.

These news agencies include ABC News, NBC News,

CNN, etc., and each of them has a sizable following

on the network. To build a complete data set, the

researchers used a network crawler tool (TikTok API)

to collect all the contents from the first video to the

Category

Number of

videos

(

n = 100

)

Median:

Number of likes

Comed

/Joke 33 2,300,000

Musical 14 2,200,000

Art & Architecture 14 1,850,000

Dance 9 2,300,000

Relationship 9 1,800,000

Animals 8 1,700,000

Challenges 8 2,400,000

Humanity/Charity 8 2,850,000

DIY & Tutorials 7 1,900,000

Skills 7 2,000,000

Not assignable 6 2,300,000

Fails & Spitefulness 5 2,600,000

Tables 1 and 2 present the distribution of

popularity indicators and the correlation between

parameters for the videos (N = 1,000). The results

show that although some videos received a large

number of interactions, the number of interactions of

the videos recommended by the recommendation

system was very uneven, with obvious peaks and

troughs. From the results in Table 3, the comedy

category maintained the leading position in terms of

popularity and frequency, while the music

performance and art architecture categories followed

closely.

3.2 Analysis of the Videos Posted on

TikTok

Table 3 conducts a linear regression analysis of

various predictive factors and TikTok video user

engagement. The main predictive variables include

the time since the video was uploaded, video length,

second-person perspective, and video sentiment. The

evaluated indicators are: Like-To-View Ratio (LTV),

Comment-To-View Ratio (CTV), and Share-To-

View Ratio (STV).

Table 3: Linear regression analysis among all TikTok news videos.

Predictors

DV: Like-To-View Ratio DV: Comment-to-View Ratio DV: Share-To-View Ratio

(LTV) (CTV) (STV)

Time since poste

0.368 0.020 0.167

Video len

th 0.053 0.037 0.077

Secon

erson view 0.150 0.062 0.034

Video sentiment -0.073 -0.054 -0.036

Total R2 (%) 15.2% 0.9% 3.0%

Note. N (all TikTok news videos) = 49,782. ***p < .001.

From Table 3, it can be seen that the duration since

the release is positively correlated with all three

engagement indicators, indicating that the longer the

video is released, the higher the audience engagement

is generally. Although the video length has a

relatively small impact on each engagement indicator,

it still shows a positive relationship, suggesting that

longer videos may receive higher interactions. The

second-person perspective is significantly positively

correlated with all engagement indicators, especially

in terms of "like" and "comment", suggesting that

TikTok videos with the second-person perspective

lead to higher audience engagement. Video emotions

show a negative relationship; that is, the stronger the

positive emotions in the video, the lower the

engagement is.

LTV (like-watch ratio) shows a negative

relationship (β = -0.073, p < .001), indicating that

more positive emotions are associated with a lower

like rate; the negative relationship of CTV (comment-

watch ratio) is also significant (β = -0.054, p < .001);

STV (share-watch ratio) also shows a similar negative

relationship (β = -0.036, p < .001); the second-person

perspective is positively correlated with LTV, CTV,

and STV (β = 0.150, 0.062, 0.034 respectively and all

p < .001), indicating higher user engagement.

The above data indicate that videos released

earlier, longer videos, videos with a second-person

perspective, and videos with negative emotions can

more effectively attract audiences.

4 DISCUSSION

TikTok possesses the most advanced algorithm

system (Siles et al., 2024), which predicts users'

interests by mining their personal information and

continuously provides them with videos. Thus, users

can focus on using and enjoying the application. This

is exactly the same as Meng and Leung (2021) and

others' research. TikTok can enhance its deep

interaction with users by using methods such as

portrait tracking and augmented reality effects to

shape more imaginative images, hierarchical interest

tag trees, user roles, etc. This article examines data

from three sources: device and account settings

(language, nation, device type, etc.); video

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information (titles, subtitles, tags, etc.); and user

involvement and participation activities (likes, shares,

comments, etc.). These three factors are consistent

with the main influencing factors of TikTok’s

recommendation system as mentioned by Cheng and

Li (2024).

Likes are an important indicator for measuring the

popularity and user engagement of short videos. They

not only reflect users' recognition of the content but

also affect the dissemination and recommendation

mechanisms of the videos. Based on this, an attempt

is made to analyze the factors influencing likes on

TikTok.

Video type is one of the primary factors affecting

likes. On TikTok, different types of videos attract

different audience groups, and thus, their likes vary

significantly. Comedy videos, due to their light-

hearted and humorous nature, usually quickly attract

the audience's attention and thus receive more likes

(Sun, Zheng and Wu, 2023). Humorous content can

touch the audience's emotions, evoke resonance, and

prompt them to like.

From the general public's perspective, the core

feature of short video platforms lies in quick

consumption, and users' attention is relatively short.

Therefore, shorter videos are more likely to attract

audiences. However, experimental results show that

longer videos are more likely to increase audience

interaction and participation (Sun, Zheng and Wu,

2023). Video emotions are also important

psychological factors affecting user likes. Videos

featuring negative emotions are more likely to

encourage user participation, according to

experimental studies.

Second-person perspective videos can

significantly increase user interaction rates, including

likes, comments, and shares. It refers to videos shot

from another angle or perspective. This way of

shooting through the use of perspective differences

can provide viewers with different viewing

experiences and increase the novelty and interest of

the content. It can make viewers feel as if they are in

the scene, thereby enhancing the viewing experience.

This immersion can increase users' attention and the

possibility of likes.

It is worth noting that on TikTok, video tags

actually exist as a genuine functional organizational

principle, which helps users find, follow, and share

information. To a certain extent, it can improve the

credibility and dissemination degree of activity

information and has an undeniable impact on likes

(Herrman, 2019). Furthermore, Chen et al. (2021)

noted that the more likes a video receives, the longer

its title.

Finally, due to the rapid development of

technology and the transformation of entertainment

methods this year, TikTok’s popularity has

fluctuated. This has also affected the analysis of the

factors influencing likes. Moreover, this study

discusses the video types that dominate people's lives,

but this does not cover all aspects. Future research can

conduct in-depth exploration of the above-related

variables to facilitate in-depth research on likes.

5 CONCLUSION

This study explores the main factors influencing the

number of likes on Douyin. Firstly, comedy videos,

due to their light-hearted and humorous nature, may

typically grab the audience's attention right away and

get a lot of likes. Secondly, contrary to the common

perception of the public, the experimental results

show that longer videos are more likely to increase the

audience's interaction participation. The emotions

conveyed by the videos can directly affect the

audience's viewing experience and participation and

are also important psychological factors for users to

like. Videos with negative emotions are more likely

to stimulate users' interaction sense. Finally, the

second-person perspective, that is, videos shot from

another angle or perspective, can provide an

immersive experience, increase the interest and

novelty of the content, and thereby increase users'

participation and liking intention. Overall, when

creators produce short videos, they should

comprehensively consider these factors to enhance

the attractiveness of the content and the interactivity

of users. By optimizing these aspects, it is possible to

effectively boost the quantity of likes on the videos

and thereby promote their dissemination effect.

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