126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
import pandas as pd
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import re
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from collections import Counter
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class InteractionAnalysis:
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def __init__(self, df: pd.DataFrame, word_exclusions: set[str]):
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self.df = df
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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 _vocab_richness_per_user(self, min_words: int = 20, top_most_used_words: int = 100) -> list:
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df = self.df.copy()
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df["content"] = df["content"].fillna("").astype(str).str.lower()
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df["tokens"] = df["content"].apply(self._tokenize)
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rows = []
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for author, group in df.groupby("author"):
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all_tokens = [t for tokens in group["tokens"] for t in tokens]
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total_words = len(all_tokens)
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unique_words = len(set(all_tokens))
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events = len(group)
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# Min amount of words for a user, any less than this might give weird results
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if total_words < min_words:
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continue
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# 100% = they never reused a word (excluding stop words)
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vocab_richness = unique_words / total_words
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avg_words = total_words / max(events, 1)
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counts = Counter(all_tokens)
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top_words = [
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{"word": w, "count": int(c)}
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for w, c in counts.most_common(top_most_used_words)
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]
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rows.append({
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"author": author,
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"events": int(events),
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"total_words": int(total_words),
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"unique_words": int(unique_words),
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"vocab_richness": round(vocab_richness, 3),
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"avg_words_per_event": round(avg_words, 2),
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"top_words": top_words
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})
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rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
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return rows
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def top_users(self) -> list:
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counts = (
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self.df.groupby(["author", "source"])
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.size()
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.sort_values(ascending=False)
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)
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top_users = [
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{"author": author, "source": source, "count": int(count)}
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for (author, source), count in counts.items()
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]
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return top_users
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def per_user_analysis(self) -> dict:
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per_user = (
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self.df.groupby(["author", "type"])
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.size()
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.unstack(fill_value=0)
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)
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# ensure columns always exist
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for col in ("post", "comment"):
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if col not in per_user.columns:
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per_user[col] = 0
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per_user["comment_post_ratio"] = per_user["comment"] / per_user["post"].replace(0, 1)
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per_user["comment_share"] = per_user["comment"] / (per_user["post"] + per_user["comment"]).replace(0, 1)
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per_user = per_user.sort_values("comment_post_ratio", ascending=True)
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per_user_records = per_user.reset_index().to_dict(orient="records")
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vocab_rows = self._vocab_richness_per_user()
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vocab_by_author = {row["author"]: row for row in vocab_rows}
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# merge vocab richness + per_user information
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merged_users = []
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for row in per_user_records:
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author = row["author"]
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merged_users.append({
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"author": author,
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"post": int(row.get("post", 0)),
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"comment": int(row.get("comment", 0)),
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"comment_post_ratio": float(row.get("comment_post_ratio", 0)),
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"comment_share": float(row.get("comment_share", 0)),
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"vocab": vocab_by_author.get(author)
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})
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merged_users.sort(key=lambda u: u["comment_post_ratio"])
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return merged_users
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def interaction_graph(self):
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interactions = {a: {} for a in self.df["author"].dropna().unique()}
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# reply_to refers to the comment id, this allows us to map comment ids to usernames
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id_to_author = self.df.set_index("id")["author"].to_dict()
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for _, row in self.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 |