From efb4c8384d26702d3f0ed380e7a7f35ec35b8f3c Mon Sep 17 00:00:00 2001
From: Dylan De Faoite <dylanseandefaoite@gmail.com>
Date: Tue, 31 Mar 2026 16:40:54 +0100
Subject: [PATCH] chore(stats): remove average_thread_depth

---
 .../src/components/InteractionalStats.tsx     | 18 ++++-----------
 frontend/src/types/ApiTypes.ts                |  1 -
 server/analysis/interactional.py              | 22 -------------------
 server/analysis/stat_gen.py                   |  1 -
 4 files changed, 4 insertions(+), 38 deletions(-)

diff --git a/frontend/src/components/InteractionalStats.tsx b/frontend/src/components/InteractionalStats.tsx
index 6216340..7ec1539 100644
--- a/frontend/src/components/InteractionalStats.tsx
+++ b/frontend/src/components/InteractionalStats.tsx
@@ -99,37 +99,27 @@ const InteractionalStats = ({ data }: InteractionalStatsProps) => {
         <div style={{ ...styles.card, gridColumn: "span 12" }}>
           <h2 style={styles.sectionTitle}>Conversation Overview</h2>
           <p style={styles.sectionSubtitle}>
-            Who talks to who, and how concentrated the replies are.
+            Who talks to who, how much they interact, and how concentrated the replies are.
           </p>
         </div>
 
-        <Card
-          label="Average Reply Depth"
-          value={
-            typeof data.average_thread_depth === "number"
-              ? data.average_thread_depth.toFixed(2)
-              : "—"
-          }
-          sublabel="How deep reply chains usually go"
-          style={{ gridColumn: "span 3" }}
-        />
         <Card
           label="Users in Network"
           value={userCount.toLocaleString()}
           sublabel="Users in the reply graph"
-          style={{ gridColumn: "span 3" }}
+          style={{ gridColumn: "span 4" }}
         />
         <Card
           label="User-to-User Links"
           value={edgeCount.toLocaleString()}
           sublabel="Unique reply directions"
-          style={{ gridColumn: "span 3" }}
+          style={{ gridColumn: "span 4" }}
         />
         <Card
           label="Total Replies"
           value={interactionVolume.toLocaleString()}
           sublabel="All reply links combined"
-          style={{ gridColumn: "span 3" }}
+          style={{ gridColumn: "span 4" }}
         />
         <Card
           label="Concentrated Replies"
diff --git a/frontend/src/types/ApiTypes.ts b/frontend/src/types/ApiTypes.ts
index eb7ed4a..9492119 100644
--- a/frontend/src/types/ApiTypes.ts
+++ b/frontend/src/types/ApiTypes.ts
@@ -135,7 +135,6 @@ type ConversationConcentration = {
 };
 
 type InteractionAnalysisResponse = {
-  average_thread_depth?: number;
   top_interaction_pairs?: [[string, string], number][];
   conversation_concentration?: ConversationConcentration;
   interaction_graph: InteractionGraph;
diff --git a/server/analysis/interactional.py b/server/analysis/interactional.py
index 8d75a3c..a2bd91e 100644
--- a/server/analysis/interactional.py
+++ b/server/analysis/interactional.py
@@ -31,28 +31,6 @@ class InteractionAnalysis:
 
         return interactions
 
-    def average_thread_depth(self, df: pd.DataFrame):
-        depths = []
-        id_to_reply = df.set_index("id")["reply_to"].to_dict()
-        for _, row in df.iterrows():
-            depth = 0
-            current_id = row["id"]
-
-            while True:
-                reply_to = id_to_reply.get(current_id)
-                if pd.isna(reply_to) or reply_to == "":
-                    break
-
-                depth += 1
-                current_id = reply_to
-
-            depths.append(depth)
-
-        if not depths:
-            return 0
-
-        return round(sum(depths) / len(depths), 2)
-
     def top_interaction_pairs(self, df: pd.DataFrame, top_n=10):
         graph = self.interaction_graph(df)
         pairs = []
diff --git a/server/analysis/stat_gen.py b/server/analysis/stat_gen.py
index 4368841..f60d809 100644
--- a/server/analysis/stat_gen.py
+++ b/server/analysis/stat_gen.py
@@ -119,7 +119,6 @@ class StatGen:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
-            "average_thread_depth": self.interaction_analysis.average_thread_depth(filtered_df),
             "top_interaction_pairs": self.interaction_analysis.top_interaction_pairs(filtered_df, top_n=100),
             "interaction_graph": self.interaction_analysis.interaction_graph(filtered_df),
             "conversation_concentration": self.interaction_analysis.conversation_concentration(filtered_df)