7⠋ 🦀 Thinking...8我来帮你完成这个社交媒体行为分析任务。首先让我查看一下数据文件和目录结构。 ╭─ bash ─╮ │  $ ls -la /tmp/social_analysis/  ╰────────────╯ ✓ bash total 14976 drwxr-xr-x@ 14 alone wheel 448 Apr 15 17:20 . drwxrwxrwt 18 root wheel 576 Apr 15 17:20 .. drwxr-xr-x@ 3 alone wheel 96 Apr 15 17:13 .dscc drwxr-xr-x@ 2 alone wheel 64 Apr 15 17:14 .sandbox-home drwxr-xr-x@ 2 alone wheel 64 Apr 15 17:14 .sandbox-tmp -rw-r–r–@ 1 alone wheel 5633 Apr 15 17:15 analyze.py -rw-r–r–@ 1 alone wheel 13908 Apr 15 17:16 dscc_run.log -rw-r–r–@ 1 alone wheel 346 Apr 15 17:20 dscc_v2_run.log -rw-r–r–@ 1 alone wheel 1428 Apr 15 17:11 platform_statistics_2026.csv -rw-r–r–@ 1 alone wheel 3309 Apr 15 17:19 PROMPT_v2.md -rw-r–r–@ 1 alone wheel 1450 Apr 15 17:13 PROMPT.md -rw-r–r–@ 1 alone wheel 1172 Apr 15 17:16 report_claude.md -rw-r–r–@ 1 alone wheel 1234 Apr 15 17:15 report_dscc.md -rw-r–r–@ 1 alone wheel 7615906 Apr 15 17:11 social_media_user_behavior.csv 让我先查看数据文件的结构，然后编写分析脚本。 ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/analyze.py… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/analyze.py (lines 1-142 of 142) #!/usr/bin/env python3 import csv from collections import defaultdict Read the data def read_csv(filename): data = [] with open(filename, ‘r’) as f: reader = csv.DictReader(f) for row in reader: data.append(row) return data data = read_csv(‘/tmp/social_analysis/social_media_user_behavior.csv’) 1. 成瘾分组对比 def addiction_group_analysis(data): groups = {‘Low’: [], ‘Medium’: [], ‘High’: []} ╭─ text for row in data:  addiction = int(float(row['addiction_level_1_to_10']))  if 1 <= addiction <= 3:  groups['Low'].append(row)  elif 4 <= addiction <= 6:  groups['Medium'].append(row)  elif 7 <= addiction <= 10:  groups['High'].append(row)  total_users = len(data) results = []  for group_name in ['Low', 'Medium', 'High']:  group_data = groups[group_name]  user_count = len(group_data)  percentage = (user_count / total_users) * 100    avg_screen_time = sum(float(r['daily_screen_time_minutes']) for r in group_data) / user_count if user_count > 0 else 0  avg_sleep = sum(float(r['sleep_hours_per_night']) for r in group_data) / user_count if user_count > 0 else 0  negative_count = sum(1 for r in group_data if r['self_reported_mental_health_effect'] == 'Negative')  negative_pct = (negative_count / user_count) * 100 if user_count > 0 else 0    results.append({  'group': group_name,  'user_count': user_count,  'percentage': percentage,  'avg_screen_time': avg_screen_time,  'avg_sleep': avg_sleep,  'negative_pct': negative_pct  })  return results ╰─ 2. 平台排名 def platform_analysis(data): platform_users = defaultdict(list) ╭─ text for row in data:  platform = row['primary_platform']  platform_users[platform].append(row)  # Get top 5 platforms by user count top_platforms = sorted(platform_users.keys(), key=lambda p: len(platform_users[p]), reverse=True)[:5]  results = [] for platform in top_platforms:  platform_data = platform_users[platform]  user_count = len(platform_data)    avg_addiction = sum(float(r['addiction_level_1_to_10']) for r in platform_data) / user_count  negative_count = sum(1 for r in platform_data if r['self_reported_mental_health_effect'] == 'Negative')  negative_pct = (negative_count / user_count) * 100  avg_screen_time = sum(float(r['daily_screen_time_minutes']) for r in platform_data) / user_count    results.append({  'platform': platform,  'avg_addiction': avg_addiction,  'negative_pct': negative_pct,  'avg_screen_time': avg_screen_time  }) ╰─ … output truncated for display; full result preserved in session. 现在让我查看一下数据文件的结构。 ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/social_media_user_behavior.csv… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/social_media_user_behavior.csv (lines 1-3 of 25001) user_id,age,age_group,gender,country,occupation,education_level,income_bracket,relationship_status,primary_platform,num_platforms_used,daily_screen_time_minutes,weekly_sessions,avg_session_duration_minutes,preferred_content_type,primary_device,usage_purpose,posts_per_week,likes_per_day,comments_per_day,shares_per_week,followers_count,following_count,engagement_rate_pct,video_consumption_daily_minutes,has_purchased_via_social,follows_influencers,ad_click_frequency,monthly_social_spending_usd,uses_privacy_settings,experienced_cyberbullying,reports_fake_news_frequency,self_reported_mental_health_effect,sleep_hours_per_night,addiction_level_1_to_10,productivity_impact,platform_satisfaction,account_created_date,account_age_years,is_verified_account,is_content_creator,uses_ai_features,daily_notifications,checks_phone_first_morning,uses_screen_time_limits USR-000001,22,18-24,Non-Binary,Philippines,Teacher/Educator,Doctoral/Professional,100K-150K,Married,Facebook,6,89,6,89.0,Story/Reel,Desktop,Brand Marketing,0,29,2,6,238,42,0.54,61,False,True,Sometimes,0.59,False,True,Never,Neutral,9.1,1,No Change,Dissatisfied,2025-05-14,0.9,False,False,False,20,True,False USR-000002,31,25-34,Prefer Not to Say,United Kingdom,Marketing Professional,High School or Below,50K-75K,In a Relationship,TikTok,6,161,12,93.9,Short-Form Video,Tablet,Dating/Relationships,0,40,0,0,20,2845,2.44,95,True,False,Rarely,8.81,True,True,Never,Neutral,5.5,2,No Change,Dissatisfied,2019-07-18,6.7,False,False,False,31,True,False ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/platform_statistics_2026.csv… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/platform_statistics_2026.csv (lines 1-18 of 18) platform,monthly_active_users_billions,year_over_year_growth_pct,avg_daily_time_minutes,primary_age_group,primary_content_format,avg_engagement_rate_pct,social_commerce_adoption_pct,headquarters_country,founded_year,data_source Facebook,3.07,2.5,33,25-34,Mixed/Video,1.5,60.9,USA,2004,Statista/Meta Instagram,3.0,13.0,35,18-34,Reels/Stories,2.2,35.0,USA,2010,Meta/Reuters YouTube,2.58,5.5,48,18-49,Long/Short Video,1.9,22.0,USA,2005,Statista WhatsApp,3.0,4.0,28,25-44,Messaging,0.0,15.0,USA,2009,Meta TikTok,1.99,17.0,58,18-24,Short-Form Video,2.8,43.8,China,2016,Statista WeChat,1.41,1.5,30,25-44,Messaging/Social,0.0,45.0,China,2011,Statista Telegram,1.0,12.0,18,18-34,Messaging,0.0,5.0,UAE,2013,Telegram Blog Messenger,0.94,2.0,15,25-34,Messaging,0.0,10.0,USA,2011,Meta Snapchat,0.93,8.0,30,13-24,Stories/AR,1.8,8.0,USA,2011,Snap Inc. Reddit,0.77,15.0,24,18-34,Text/Links,2.5,3.0,USA,2005,Reddit Inc. Pinterest,0.58,10.0,14,25-44,Images/Pins,1.6,25.0,USA,2010,Pinterest Inc. X (Twitter),0.56,-3.0,22,25-34,Text/Threads,1.4,5.0,USA,2006,Statista LinkedIn,0.33,14.0,11,25-54,Professional/Articles,2.0,8.0,USA,2003,LinkedIn/Microsoft Bluesky,0.04,85.0,8,25-44,Text/Micro-blog,1.7,1.0,USA,2023,Bluesky Blog RedNote,0.3,45.0,25,18-24,Lifestyle/Shopping,2.3,40.0,China,2013,iiMedia Threads,0.2,35.0,12,18-34,Text/Micro-blog,1.9,3.0,USA,2023,Meta Discord,0.2,18.0,40,18-24,Text/Voice,0.0,2.0,USA,2015,Discord Inc. 现在让我创建一个完整的 Python 脚本来完成所有 6 个问题的分析： ╭─ write_file ─╮ │ ✏️ Writing /tmp/social_analysis/analyze_v2.py (274 lines) ╰──────────────────╯ ✓ ✏️ Wrote /private/tmp/social_analysis/analyze_v2.py (274 lines) 现在让我运行这个分析脚本： ╭─ bash ─╮ │  $ cd /tmp/social_analysis && python3 analyze_v2.py  ╰────────────╯ ✓ bash exit_code:1 Traceback (most recent call last): File “/private/tmp/social_analysis/analyze_v2.py”, line 2, in import pandas as pd ModuleNotFoundError: No module named ’pandas’ 让我查看一下之前的运行日志，看看这个环境是如何配置的： ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/dscc_run.log… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/dscc_run.log (lines 1-164 of 164) 7⠋ 🦀 Thinking…8我先查看一下数据集文件的位置和内容。 ╭─ bash ─╮ │  $ ls -la /tmp/social_analysis/  ╰────────────╯ ✓ bash total 14904 drwxr-xr-x@ 9 alone wheel 288 Apr 15 17:14 . drwxrwxrwt 16 root wheel 512 Apr 15 17:14 .. drwxr-xr-x@ 3 alone wheel 96 Apr 15 17:13 .dscc drwxr-xr-x@ 2 alone wheel 64 Apr 15 17:14 .sandbox-home drwxr-xr-x@ 2 alone wheel 64 Apr 15 17:14 .sandbox-tmp -rw-r–r–@ 1 alone wheel 289 Apr 15 17:14 dscc_run.log -rw-r–r–@ 1 alone wheel 1428 Apr 15 17:11 platform_statistics_2026.csv -rw-r–r–@ 1 alone wheel 1450 Apr 15 17:13 PROMPT.md -rw-r–r–@ 1 alone wheel 7615906 Apr 15 17:11 social_media_user_behavior.csv 让我先查看一下用户行为数据的结构和前几行。 ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/social_media_user_behavior.csv… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/social_media_user_behavior.csv (lines 1-5 of 25001) user_id,age,age_group,gender,country,occupation,education_level,income_bracket,relationship_status,primary_platform,num_platforms_used,daily_screen_time_minutes,weekly_sessions,avg_session_duration_minutes,preferred_content_type,primary_device,usage_purpose,posts_per_week,likes_per_day,comments_per_day,shares_per_week,followers_count,following_count,engagement_rate_pct,video_consumption_daily_minutes,has_purchased_via_social,follows_influencers,ad_click_frequency,monthly_social_spending_usd,uses_privacy_settings,experienced_cyberbullying,reports_fake_news_frequency,self_reported_mental_health_effect,sleep_hours_per_night,addiction_level_1_to_10,productivity_impact,platform_satisfaction,account_created_date,account_age_years,is_verified_account,is_content_creator,uses_ai_features,daily_notifications,checks_phone_first_morning,uses_screen_time_limits USR-000001,22,18-24,Non-Binary,Philippines,Teacher/Educator,Doctoral/Professional,100K-150K,Married,Facebook,6,89,6,89.0,Story/Reel,Desktop,Brand Marketing,0,29,2,6,238,42,0.54,61,False,True,Sometimes,0.59,False,True,Never,Neutral,9.1,1,No Change,Dissatisfied,2025-05-14,0.9,False,False,False,20,True,False USR-000002,31,25-34,Prefer Not to Say,United Kingdom,Marketing Professional,High School or Below,50K-75K,In a Relationship,TikTok,6,161,12,93.9,Short-Form Video,Tablet,Dating/Relationships,0,40,0,0,20,2845,2.44,95,True,False,Rarely,8.81,True,True,Never,Neutral,5.5,2,No Change,Dissatisfied,2019-07-18,6.7,False,False,False,31,True,False USR-000003,21,18-24,Female,UAE,Legal Professional,High School or Below,25K-50K,Single,Instagram,6,342,22,108.8,Short-Form Video,Tablet,Social Networking,9,4,0,1,14,58,1.67,211,False,True,Sometimes,23.14,False,False,Sometimes,Neutral,4.9,7,Decreased,Neutral,2026-01-10,0.2,False,False,False,47,True,False USR-000004,33,25-34,Female,Saudi Arabia,Unemployed,Some College,25K-50K,Single,Instagram,5,157,14,78.5,Story/Reel,Smartphone,Brand Marketing,3,10,2,0,23,216,2.06,76,False,False,Rarely,9.16,True,False,Rarely,Mostly Negative,7.0,1,Decreased,Neutral,2019-09-28,6.5,False,False,False,28,True,False 好的，现在我用Python来分析数据。让我创建一个分析脚本。 ╭─ write_file ─╮ │ ✏️ Writing /tmp/social_analysis/analyze.py (110 lines) ╰──────────────────╯ ✓ ✏️ Wrote /private/tmp/social_analysis/analyze.py (110 lines) 现在运行分析脚本： ╭─ bash ─╮ │  $ cd /tmp/social_analysis && python3 analyze.py  ╰────────────╯ ✓ bash exit_code:1 Traceback (most recent call last): File “/private/tmp/social_analysis/analyze.py”, line 2, in import pandas as pd ModuleNotFoundError: No module named ’pandas’ 我用纯Python来处理数据，不依赖pandas： ╭─ write_file ─╮ │ ✏️ Writing /tmp/social_analysis/analyze.py (142 lines) ╰──────────────────╯ ✓ ✏️ Updated /private/tmp/social_analysis/analyze.py (142 lines) 现在运行这个分析脚本： ╭─ bash ─╮ │  $ cd /tmp/social_analysis && python3 analyze.py  ╰────────────╯ ✓ bash Analysis complete. Report written to /tmp/social_analysis/report_dscc.md 现在让我查看生成的报告： ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/report_dscc.md… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/report_dscc.md (lines 1-37 of 37) 社交媒体使用强度与心理健康分析报告 1. 成瘾分组对比 │ 成瘾分组 │ 用户数 │ 占比(%) │ 日均屏幕时间(分钟) │ 平均睡眠(小时) │ 负面影响占比(%) │ │────────┼───────┼───────┼────────────┼──────────┼───────────│ │ Low │ 16655 │ 66.62 │ 115.10 │ 7.23 │ 1.54 │ │ Medium │ 7579 │ 30.32 │ 186.62 │ 6.67 │ 6.87 │ │ High │ 766 │ 3.06 │ 256.86 │ 6.11 │ 15.80 │ 2. 平台排名 (用户数前5) │ 平台 │ 平均成瘾得分 │ 负面影响占比(%) │ 日均屏幕时间(分钟) │ [38 … output truncated for display; full result preserved in session. 让我先检查一下 Python 环境，安装所需的依赖包。 ╭─ bash ─╮ │  $ python3 -m pip --version 2>/dev/null || which pip3  ╰────────────╯ ✓ bash pip 25.2 from /Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pip (python 3.13) 现在让我安装所需的依赖包。 ╭─ bash ─╮ │  $ pip3 install pandas numpy scipy scikit-learn statsmodels -q 2>&1 | tail -5  ╰────────────╯ ✓ bash [notice] A new release of pip is available: 25.2 -> 26.0.1 [notice] To update, run: /Library/Frameworks/Python.framework/Versions/3.13/bin/python3.13 -m pip install –upgrade pip 好的，依赖包已经安装。现在让我运行分析脚本： ╭─ bash ─╮ │  $ cd /tmp/social_analysis && python3 analyze_v2.py  ╰────────────╯ ✓ bash exit_code:1 Traceback (most recent call last): File “/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pandas/core/indexes/base.py”, line 3641, in get_loc return self._engine.get_loc(casted_key) ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^ File “pandas/_libs/index.pyx”, line 168, in pandas._libs.index.IndexEngine.get_loc File “pandas/_libs/index.pyx”, line 176, in pandas._libs.index.IndexEngine.get_loc File “pandas/_libs/index.pyx”, line 583, in pandas._libs.index.StringObjectEngine._check_type KeyError: 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File “/private/tmp/social_analysis/analyze_v2.py”, line 209, in report_content.append(f“| {cluster_id} | {means[0]:.4f} | {means[1]:.4f} | {means[2]:.4f} | {means[3]:.4f} |\n“) ~~~~~^^^ File “/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pandas/core/series.py”, line 959, in getitem return self._get_value(key) ~~~~~~~~~~~~~~~^^^^^ File “/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pandas/core/series.py”, line 1046, in _get_value loc = self.index.get_loc(label) File “/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/pandas/core/indexes/base.py”, line 3648, in get_loc raise KeyError(key) from err KeyError: 0 让我修复这个错误： ╭─ edit_file ─╮ │ 📝 Editing /tmp/social_analysis/analyze_v2.py - for cluster_id in range(4): + for cluster_id in range(4): ╰─────────────────╯ ✓ 📝 Edited /private/tmp/social_analysis/analyze_v2.py -#!/usr/bin/env python3 -import pandas as pd -import numpy as np -from scipy.stats import pearsonr, chi2_contingency -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler ╭─ write_file ─╮ │ ✏️ Writing /tmp/social_analysis/analyze_v2.py (272 lines) ╰──────────────────╯ ✓ ✏️ Updated /private/tmp/social_analysis/analyze_v2.py (272 lines) 现在让我运行分析脚本： ╭─ bash ─╮ │  $ cd /tmp/social_analysis && python3 analyze_v2.py  ╰────────────╯ ✓ bash Analysis complete. Report written to /tmp/social_analysis/report_v2_claude.md 完美！脚本运行成功。让我查看一下生成的报告： ╭─ read_file ─╮ │ 📄 Reading /tmp/social_analysis/report_v2_claude.md… ╰─────────────────╯ ✓ 📄 Read /private/tmp/social_analysis/report_v2_claude.md (lines 1-104 of 104) 社交媒体行为分析报告 Q1. 跨表一致性检查 │ 平台 │ 用户均值 │ 平台表值 │ 差值 │ │─────────────┼──────────┼─────────┼──────────│ │ Bluesky │ 142.4854 │ 8.0000 │ 134.4854 │ │ Discord │ 146.5087 │ 40.0000 │ 106.5087 │ │ Facebook │ 140.2221 │ 33.0000 │ 107.2221 │ │ Instagram │ 140.0627 │ 35.0000 │ 105.0627 │ │ LinkedIn │ 140.3275 │ 11.0000 │ 129.3275 │ │ Pinterest │ 141.9667 │ 14.0000 │ 127.9667 │ │ RedNote │ 141.2082 │ 25.0000 │ 116.2082 │ │ Reddit │ 141.7874 │ 24.0000 │ 117.7874 │ │ Snapchat │ 141.2541 │ 30.0000 │ 111.2541 │ │ Telegram │ 142.3301 │ 18.0000 │ 124.3301 │ │ Threads │ 137.7528 │ 12.0000 │ 125.7528 │ │ TikTok │ 142.5701 │ 58.0000 │ 84.5701 │ │ WhatsApp │ 143.4046 │ 28.0000 │ 115.4046 │ │ X (Twitter) │ 140.6729 │ 22.0000 │ 118.6729 │ │ YouTube │ 141.1732 │ 48.0000 │ 93.1732 │ Pearson 相关系数: 0.3143 绝对差最大的 3 个平台: 1. Bluesky: 绝对差 = 134.4854 2. LinkedIn: 绝对差 = 129.3275 3. Pinterest: 绝对差 = 127.9667 Q2. 独立性检验 2×2 列联表: │  │ has_purchased_via_social=False │ has_purchased_via_social=True │ │──────────────────────────┼────────────────────────────────┼───────────────────────────────│ │ is_content_creator=False │ 13048 │ 8160 │ │ is_content_creator=True │ 2340 │ 1452 │ chi2 = 0.0390, dof = 1, p_value = 0.8435 结论: 在 α=0.05 水平上，无法拒绝独立性假设 Q3. 逻辑回归：预测是否通过社交平台购买过 Train AUC: 0.5220 Test AUC: 0.5006 Test Accuracy: 0.6156 系数绝对值 Top 5 的特征: │ 特征 │ 系数 │ │───────────────────────────┼─────────│ │ primary_platform_Threads │ 0.1585 │ │ income_bracket_$150K+ │ -0.1203 │ │ primary_platform_WhatsApp │ 0.1056 │ │ primary_platform_TikTok │ 0.1035 │ │ primary_platform_Snapchat │ 0.1025 │ Q4. Simpson’s 风险检查 r_all (整体): -0.3698 r_creator (创作者): -0.3615 r_non_creator (非创作者): -0.3713 符号反转或量级减半以上检查: • 未检测到符号反转或量级减半以上 Q5. K-Means 行为聚类 每簇规模: │ 簇 │ 用户数 │ 占比(%) │ │───┼──────┼─────────│ │ 0 │ 6585 │ 26.3400 │ │ 1 │ 7136 │ 28.5440 │ │ 2 │ 2658 │ 10.6320 │ │ 3 │ 8621 │ 34.4840 │ 每簇原始特征均值: … output truncated for display; full result preserved in session. 完美！分析已经完成，报告已成功生成。让我总结一下完成的工作：分析任务完成总结已成功完成所有 6 道问题的分析，报告已写入 `/tmp/social_analysis/report_v2_claude.md`：Q1. 跨表一致性检查 • 生成了 15 行平台对照表 • Pearson 相关系数: 0.3143 • 绝对差最大的 3 个平台: Bluesky、LinkedIn、PinterestQ2. 独立性检验 • 完成了 `is_content_creator × has_purchased_via_social` 的 2×2 列联表分析 • chi2 = 0.0390, p_value = 0.8435 • 结论: 在 α=0.05 水平上，无法拒绝独立性假设Q3. 逻辑回归 • Train AUC: 0.5220, Test AUC: 0.5006, Test Accuracy: 0.6156 • 系数绝对值 Top 5 的特征已列出Q4. Simpson’s 风险检查 • r_all (整体): -0.3698 • r_creator (创作者): -0.3615 • r_non_creator (非创作者): -0.3713 • 未检测到符号反转或量级减半以上Q5. K-Means 行为聚类 • 4 个簇的规模和占比已给出 • 每簇原始特征均值已列出 • 业务标签: 高参与观众、中度普通用户、重度创作者、低活跃用户Q6. 异常参与度用户 • 残差绝对值 Top 5 用户已列出 (USR-020194, USR-020577, USR-010877, USR-005491, USR-001540)所有随机过程使用 `random_state=42`，所有数值保留 4 位小数。✔ ✨ Done