Dylan De Faoite
3e78a54388
Remove old `initiator_ratio` metric which wasn't working due every event having a `reply_to` value. This metric was suggested by AI, and is a surprisingly interesting one that gave interesting insights.
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import pandas as pd
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import re
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class InteractionAnalysis:
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def __init__(self, word_exclusions: set[str]):
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self.word_exclusions = word_exclusions
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def _tokenize(self, text: str):
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tokens = re.findall(r"\b[a-z]{3,}\b", text)
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return [t for t in tokens if t not in self.word_exclusions]
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def interaction_graph(self, df: pd.DataFrame):
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interactions = {a: {} for a in df["author"].dropna().unique()}
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# reply_to refers to the comment id, this allows us to map comment/post ids to usernames
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id_to_author = df.set_index("post_id")["author"].to_dict()
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for _, row in df.iterrows():
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a = row["author"]
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reply_id = row["reply_to"]
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if pd.isna(a) or pd.isna(reply_id) or reply_id == "":
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continue
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b = id_to_author.get(reply_id)
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if b is None or a == b:
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continue
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interactions[a][b] = interactions[a].get(b, 0) + 1
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return interactions
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def average_thread_depth(self, df: pd.DataFrame):
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depths = []
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id_to_reply = df.set_index("id")["reply_to"].to_dict()
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for _, row in df.iterrows():
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depth = 0
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current_id = row["id"]
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while True:
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reply_to = id_to_reply.get(current_id)
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if pd.isna(reply_to) or reply_to == "":
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break
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depth += 1
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current_id = reply_to
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depths.append(depth)
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if not depths:
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return 0
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return round(sum(depths) / len(depths), 2)
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def top_interaction_pairs(self, df: pd.DataFrame, top_n=10):
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graph = self.interaction_graph(df)
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pairs = []
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for a, targets in graph.items():
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for b, count in targets.items():
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pairs.append(((a, b), count))
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pairs.sort(key=lambda x: x[1], reverse=True)
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return pairs[:top_n]
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def conversation_concentration(self, df: pd.DataFrame) -> dict:
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if "type" not in df.columns:
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return {}
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comments = df[df["type"] == "comment"]
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if comments.empty:
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return {}
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author_counts = comments["author"].value_counts()
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total_comments = len(comments)
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total_authors = len(author_counts)
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top_10_pct_n = max(1, int(total_authors * 0.1))
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top_10_pct_share = round(author_counts.head(top_10_pct_n).sum() / total_comments, 4)
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return {
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"total_commenting_authors": total_authors,
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"top_10pct_author_count": top_10_pct_n,
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"top_10pct_comment_share": float(top_10_pct_share),
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"single_comment_authors": int((author_counts == 1).sum()),
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"single_comment_author_ratio": float(round((author_counts == 1).sum() / total_authors, 4)),
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} |