| import pandas as pd
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| import numpy as np
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| import random
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|
|
| random.seed(42)
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| np.random.seed(42)
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|
|
| n = 10000
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|
|
| data = []
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|
|
| for _ in range(n):
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|
|
|
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| last_message_length = random.randint(1, 50)
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| response_time_gap = random.uniform(0, 72)
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| initiator = random.choice(['me', 'them'])
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| conversation_length = random.randint(1, 200)
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| reply_ratio = round(random.uniform(0, 1), 2)
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| avg_response_time = random.uniform(1, 120)
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| message_tone = random.choice(['dry', 'neutral', 'enthusiastic'])
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| emoji_count = random.randint(0, 10)
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| question_asked = random.choice([0, 1])
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| time_of_day = random.choice(['day', 'night'])
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| seen_ignored = random.choice([0, 1])
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| past_ghosting_history = random.choice([0, 1])
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|
|
|
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| user_type = random.choice(['interested', 'casual', 'dry_texter', 'ghoster'])
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|
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|
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| reply_prob = 0.5
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| if seen_ignored == 1 and response_time_gap > 24: reply_prob -= 0.35
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| if message_tone == 'enthusiastic': reply_prob += 0.20
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| if question_asked == 1: reply_prob += 0.15
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| if response_time_gap > 24: reply_prob -= 0.20
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|
|
|
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| if user_type == 'interested': reply_prob += 0.25
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| elif user_type == 'casual': reply_prob += 0.05
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| elif user_type == 'dry_texter': reply_prob -= 0.10
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| elif user_type == 'ghoster': reply_prob -= 0.30
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|
|
| reply_prob = max(0.05, min(reply_prob, 0.95))
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| reply = 1 if random.random() < reply_prob else 0
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|
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|
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| ghost_prob = 0.20
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|
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|
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| if response_time_gap > 48: ghost_prob += 0.28
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| elif response_time_gap > 24: ghost_prob += 0.15
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|
|
|
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| if reply_ratio < 0.2: ghost_prob += 0.22
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| elif reply_ratio < 0.4: ghost_prob += 0.12
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|
|
|
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| if message_tone == 'dry': ghost_prob += 0.15
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| elif message_tone == 'neutral': ghost_prob += 0.05
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|
|
|
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| if seen_ignored == 1: ghost_prob += 0.18
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|
|
|
|
| if past_ghosting_history == 1: ghost_prob += 0.15
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|
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|
|
| combo_bonus = 0.0
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| if reply_ratio < 0.3 and response_time_gap > 24:
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| combo_bonus += 0.15
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|
|
|
|
| decay = min(conversation_length / 200, 1.0)
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| combo_bonus += decay * 0.10
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|
|
|
|
| if message_tone == 'enthusiastic' and reply_ratio < 0.3:
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| combo_bonus += 0.08
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|
|
|
|
| if abs(response_time_gap - avg_response_time) > 20:
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| combo_bonus += 0.08
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|
|
|
|
| combo_bonus = min(combo_bonus, 0.25)
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| ghost_prob += combo_bonus
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|
|
|
|
| if user_type == 'interested': ghost_prob -= 0.18
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| elif user_type == 'casual': ghost_prob += 0.05
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| elif user_type == 'dry_texter': ghost_prob += 0.12
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| elif user_type == 'ghoster': ghost_prob += 0.28
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|
|
| ghost_prob = max(0.05, min(ghost_prob, 0.92))
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| ghosted = 1 if random.random() < ghost_prob else 0
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|
|
|
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| if random.random() < 0.02: reply = 1 - reply
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| if random.random() < 0.02: ghosted = 1 - ghosted
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|
|
| data.append([
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| last_message_length, response_time_gap, initiator,
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| conversation_length, reply_ratio, avg_response_time,
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| message_tone, emoji_count, question_asked, time_of_day,
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| seen_ignored, past_ghosting_history, user_type,
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| reply, ghosted
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| ])
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|
|
| columns = [
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| 'last_message_length', 'response_time_gap', 'initiator',
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| 'conversation_length', 'reply_ratio', 'avg_response_time',
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| 'message_tone', 'emoji_count', 'question_asked', 'time_of_day',
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| 'seen_ignored', 'past_ghosting_history',
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| 'user_type',
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| 'reply', 'ghosted'
|
| ]
|
|
|
| df = pd.DataFrame(data, columns=columns)
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| df.to_csv("ghosting_dataset5.csv", index=False)
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|
|
| print(df.shape)
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| print("\nClass distribution (ghosted):")
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| print(df['ghosted'].value_counts())
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| print(f"Ghosting rate: {df['ghosted'].mean():.1%}")
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| print("\nClass distribution (reply):")
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| print(df['reply'].value_counts())
|
| print(f"Reply rate: {df['reply'].mean():.1%}")
|
| print("\nUser type distribution:")
|
| print(df['user_type'].value_counts()) |
