What Types of Douyin Videos Really Capture Millions of Views? A Data‑Driven Deep Dive

01 Data Acquisition

Data was obtained from a third‑party monitoring service covering more than 5,000 Douyin "big V" accounts. The CSV includes fields such as nickname, gender, location, category, likes, followers, video count, comments, shares, following count, graduation school, verification, and biography.

02 Data Visualization

Python libraries (pandas, pyecharts, matplotlib, wordcloud) were used to visualize the dataset.

Gender Distribution

After removing unknown values, the gender ratio is roughly 1:1.

Likes – Top 10

The top‑10 liked videos are dominated by news media accounts; many received over 100 million likes during the pandemic.

Fans – Distribution

"People's Daily" and "CCTV News" each have close to 120 million followers, while many accounts have only a few thousand.

Comments – Top 10

Most commented videos belong to the news media category.

Shares – Top 10

Users prefer sharing news and food‑related videos.

Likes by Content Type

A treemap shows that beauty, humor, gaming, and drama videos attract the most likes.

Average Likes per Video – Top 10

Li Xian ranks first in average likes per video.

Geographic Distribution – Provinces

Guangdong, Zhejiang and Sichuan have the highest number of Douyin big V accounts.

Geographic Distribution – Cities

Beijing leads, followed by Hangzhou.

International Presence

The United States has the most foreign Douyin big V accounts.

Graduation Schools – Top 10

Beijing Film Academy, Communication University of China, Zhejiang Media College, and other performing‑arts schools dominate.

Verification Status

Most big V accounts have a verified status other than "unknown".

Bio Word Cloud

A word cloud of the biographies highlights keywords such as "business cooperation" and "content creator".

Code Samples

The following Python code snippets perform the data loading, processing, and visualizations described above.

from pyecharts.charts import Pie, Bar, TreeMap, Map, Geo
from wordcloud import WordCloud, ImageColorGenerator
from pyecharts import options as opts
import matplotlib.pyplot as plt
from PIL import Image
import pandas as pd
import numpy as np
import jieba

df = pd.read_csv('douyin.csv', header=0, encoding='utf-8-sig')
print(df)

def create_gender(df):
    df = df.copy()
    df.loc[df.gender == '0', 'gender'] = '未知'
    df.loc[df.gender == '1', 'gender'] = '男性'
    df.loc[df.gender == '2', 'gender'] = '女性'
    gender_com = df.groupby('gender')['gender'].agg(['count']).reset_index()
    attr = gender_com['gender']
    v1 = gender_com['count']
    pie = Pie(init_opts=opts.InitOpts(width="800px", height="400px"))
    pie.add('', [list(z) for z in zip(attr, v1)], radius=["40%", "75%"])
    pie.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V性别分布情况", pos_left="center", pos_top="top"),
                         legend_opts=opts.LegendOpts(orient="vertical", pos_left="left"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}))
    pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, formatter="{b}:{d}%"))
    pie.render("抖音大V性别分布情况.html")

def create_likes(df):
    df = df.sort_values('likes', ascending=False)
    attr = df['name'][0:10]
    v1 = [float('%.1f' % (i/100000000)) for i in df['likes'][0:10]]
    bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
    bar.add_xaxis(list(reversed(attr.tolist())))
    bar.add_yaxis('', list(reversed(v1)))
    bar.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V点赞数TOP10(亿)", pos_left="center", pos_top="18"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}),
                         xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)))
    bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="right", color="black"))
    bar.reversal_axis()
    bar.render("抖音大V点赞数TOP10(亿).html")

def create_type_likes(df):
    likes_type_message = df.groupby('category')
    likes_type_com = likes_type_message['likes'].agg(['sum']).reset_index()
    dom = []
    for name, num in zip(likes_type_com['category'], likes_type_com['sum']):
        dom.append({'name': name, 'value': num})
    treemap = TreeMap(init_opts=opts.InitOpts(width="800px", height="400px"))
    treemap.add('', dom)
    treemap.set_global_opts(title_opts=opts.TitleOpts(title="各类型抖音大V点赞数汇总图", pos_left="center", pos_top="5"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}),
                         legend_opts=opts.LegendOpts(is_show=False))
    treemap.render("各类型抖音大V点赞数汇总图.html")

def create_avg_likes(df):
    df = df[df['videos'] > 0]
    df.eval('result = likes/(videos*10000)', inplace=True)
    df['result'] = df['result'].round(1)
    df = df.sort_values('result', ascending=False)
    attr = df['name'][0:10]
    v1 = ['%.1f' % i for i in df['result'][0:10]]
    bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
    bar.add_xaxis(list(reversed(attr.tolist())))
    bar.add_yaxis('', list(reversed(v1)))
    bar.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V平均视频点赞数TOP10(万)", pos_left="center", pos_top="18"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}),
                         xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)))
    bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="right", color="black"))
    bar.reversal_axis()
    bar.render("抖音大V平均视频点赞数TOP10(万).html")

def create_province_map(df):
    df = df[df["country"] == "中国"]
    df1 = df.copy()
    df1["province"] = df1["province"].str.replace("省", "").str.replace("壮族自治区", "").str.replace("维吾尔自治区", "").str.replace("自治区", "")
    df_num = df1.groupby("province")["province"].agg(count="count")
    provinces = df_num.index.tolist()
    counts = df_num["count"].values.tolist()
    map = Map(init_opts=opts.InitOpts(width="800px", height="400px"))
    map.add('', [list(z) for z in zip(provinces, counts)], "china")
    map.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V省份分布情况", pos_left="center", pos_top="0"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}),
                         visualmap_opts=opts.VisualMapOpts(max_=600, is_piecewise=False))
    map.render("抖音大V省份分布情况.html")

def create_city(df):
    df1 = df[df["country"] == "中国"].copy()
    df1["city"] = df1["city"].str.replace("市", "")
    df_num = df1.groupby("city")["city"].agg(count="count").reset_index().sort_values(by="count", ascending=False)
    cities = df_num[:10]["city"].values.tolist()
    counts = df_num[:10]["count"].values.tolist()
    bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px"))
    bar.add_xaxis(cities)
    bar.add_yaxis('', counts)
    bar.set_global_opts(title_opts=opts.TitleOpts(title="抖音大V城市分布TOP10", pos_left="center", pos_top="18"),
                         toolbox_opts=opts.ToolboxOpts(is_show=True, feature={"saveAsImage": {}}),
                         yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)))
    bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="top", color="black"))
    bar.render("抖音大V城市分布TOP10.html")

def create_wordcloud(df, picture):
    words = pd.read_csv('chineseStopWords.txt', encoding='gbk', sep='\t', names=['stopword'])
    text = ''
    df1 = df[df["signature"] != ""].copy()
    for line in df1['signature']:
        text += ' '.join(jieba.cut(str(line).replace(' ', ''), cut_all=False))
    stopwords = set(words['stopword'])
    backgroud_Image = plt.imread('douyin.png')
    alice_coloring = np.array(Image.open(r"douyin.png"))
    image_colors = ImageColorGenerator(alice_coloring)
    wc = WordCloud(background_color='white', mask=backgroud_Image, font_path='方正兰亭刊黑.TTF',
                   max_words=2000, max_font_size=70, min_font_size=1, prefer_horizontal=1,
                   color_func=image_colors, random_state=50, stopwords=stopwords, margin=5)
    wc.generate_from_text(text)
    plt.imshow(wc)
    plt.axis('off')
    wc.to_file(picture)
    print('生成词云成功!')