Preface
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Python analysis of Douyin user behavior data video explanation address
https://www.bilibili.com/video/BV1yp4y1q7ZC/Copy code
With the rise of short video apps, in the short video social market, Douyin short videos are extremely popular. We hope that through this analysis, we can give users some suggestions for posting videos.
data analysis
1 platform
Daily broadcasts, daily users, daily authors, daily works
The trend of daily broadcast volume, daily user volume, daily author volume, and daily production volume over time is basically the same: steady growth; during the period from 2019-10-20 to 2019-10-29, all indicators have experienced huge growth first. After that, it tends to be stable, and then falls back to the normal level. It is guessed that at this point in time the platform has carried out promotion activities, so that it has attracted a large number of users.
Author: The number of works, works won praise rate
The number of author’s works is directly proportional to the broadcast rate
The number of author’s works and the like rate do not have much relationship
Author: Play the amount of contribution
3,500, 18% or so authors contributed 80% of the broadcast volume of the platform, obeying the 28th rule.
2 works
Source of works
It can be seen that a large number of sources and channels of works are 0, accounting for 98.48%.
Selected songs top10
The top ten background music IDs are: 22, 220, 25, 68, 110, 33, 468, 57, 43, 238 (there is no extra information to check the corresponding song name)
From 2019-10-21 to 2019-10-29 mentioned above, the playback volume of all song works has increased, among which songs with IDs 22, 220, 68, 25 have a skyrocketing trend.
Song and like rate, completion rate
Picture uploading
There is little difference between the like rate and the completion rate of different background music works, that is, there is little difference between the like and the complete playback result after the playback volume is generated.
The play volume of different background music works varies greatly, and the play volume of individual songs is outstanding
Combined with the above picture, most of the songs played on the platform are a small part of popular songs.
There is little difference in time between the like rate and the completion rate of different songs
The relationship between the duration of different works and the volume of products and playback
There is a normal proportional relationship between product volume and playback volume of different durations
The product volume (play volume) with a duration of 7-12s accounts for the majority
Play volume above 23s is basically 0
Work duration and completion rate, like rate
The broadcast rate is generally stable at about 0.4 within 2s-43s, and fluctuates greatly after 43s;
The like rate basically maintains within 0.6 up and down within 2s-43s, and fluctuates greatly after 43s.
Work release time (24H)
The volume of products in different periods is basically proportional to the volume of playback
10-17 time period, the amount of works and broadcasts on the platform is low (work/study time)
19-0-5 The playback volume of the entire time period is relatively high.
Work release time and completion rate, like rate
In the 0-5 time period, the completion rate and the like rate of the work are higher
to sum up
Summary of analysis results
platform:
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Increase event promotion: attract new users and keep old users
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Increase author incentive projects: encourage authors to publish works
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Expansion channels: attract new users
Author:
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Channel: 0
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Background music: popular songs
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Work duration: 7-12s, preferably not more than 23s
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Work release time: 19-0-5 points, of which 0-5 points are better
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Actively participate in platform activities
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