diff --git a/article/proceedings.tex b/article/proceedings.tex
index 5dd59e79c28141fc24f2becc9babe074310b3db3..4167ad8d30f5241f2a0a3cdb6166ac25e273eecf 100644
--- a/article/proceedings.tex
+++ b/article/proceedings.tex
@@ -725,13 +725,16 @@ The edit is not saved.
 
 \textbf{Interesting questions}
 \begin{itemize}
-    \item how many filters are there (were there over the years): 954 filters (stand: 06.01.2019); TODO: historically?
+    \item how many filters are there (were there over the years): 954 filters (stand: 06.01.2019); TODO: historically?; This includes deleted filters
     \item what do the most active filters do?: see~\ref{tab:most-active-actions}
     \item get a sense of what gets filtered (more qualitative): TODO: refine after sorting through manual categories; preliminary: vandalism; unintentional suboptimal behavior from new users who don't know better ("good faith edits") such as blanking an article/section; creating an article without categories; adding larger texts without references; large unwikified new article (180); or from users who are too lazy (to write proper edit summaries; editing behaviours and styles not suitable for an encyclopedia (poor grammar/not commiting to orthography norms; use of emoticons and !; ascii art?); "unexplained removal of sourced content" (636) may be an attempt to silence a view point the editor doesn't like; self-promotion(adding unreferenced material to BLP; "users creating autobiographies" 148;); harassment; sockpuppetry; potential copyright violations; that's more or less it actually. There's a third bigger cluster of maintenance stuff, such as tracking bugs or other problems, trying to sort through bot edits and such. For further details see the jupyter notebook.
+        Interestingly, there was a guideline somewhere stating that no trivial behaviour should trip filters (e.g. starting every paragraph with a small letter;) I actually think, a bot fixing this would be more appropriate.
     \item has the willingness of the community to use filters increased over time?: looking at aggregated values of number of triggered filters per year, the answer is rather it's quite constant; TODO: plot it at a finer granularity
+        when aggregating filter triggers per month, one notices that there's an overall slight upward tendency.
+        Also, there is a dip in the middle of 2014 and a notable peak at the beginning of 2016, that should be investigated further.
     \item how often were (which) filters triggered: see \url{filter-lists/20190106115600_filters-sorted-by-hits.csv} and~\ref{tab:most-active-actions}; see also jupyter notebook for aggregated hitcounts over tagged categories
     \item percentage of triggered filters/all edits; break down triggered filters according to typology: TODO still need the complete abuse\_filter\_log table!; and probably further dumps in order to know total number of edits
-    \item percentage filters of different types over the years: TODO according to actions (I need a complete abuse\_filter\_log table for this!); according to self-assigned tags (finish tagging!)
+    \item percentage filters of different types over the years: according to actions (I need a complete abuse\_filter\_log table for this!); according to self-assigned tags %TODO plot!
     \item what gets classified as vandalism? has this changed over time? TODO: (look at words and patterns triggered by the vandalism filters; read vandalism policy page); pay special attention to filters labeled as vandalism by the edit filter editors (i.e. in the public description) vs these I labeled as vandalism
 \end{itemize}
 
@@ -755,7 +758,7 @@ The edit is not saved.
 \textbf{Questions on abuse\_filter\_log table}
 \begin{itemize}
     \item how often were filters with different actions triggered? (afl\_actions)
-    \item what types of users trigger the filters (IPs? registered?)
+    \item what types of users trigger the filters (IPs? registered?) : IPs: 16,489,266, logged in users: 6,984,897 (Stand 15.03.2019);
     \item on what articles filters get triggered most frequently (afl\_title)
     \item what types of user actions trigger filters most frequently? (afl\_action) (edit, delete, createaccount, move, upload, autocreateaccount, stashupload)
     \item in which namespaces get filters triggered most frequently?
diff --git a/src/explore.ipynb b/src/explore.ipynb
index b5330554cb455cd0f5df72221d5f3665275f0ecf..ba599cf0d143130bfbdfd3a099501febf00b094c 100644
--- a/src/explore.ipynb
+++ b/src/explore.ipynb
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,6 +311,192 @@
     "# \"The group this filter belongs to, as defined in $wgAbuseFilterValidGroups.\" still don't get it"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Timestamp\n",
+    "\n",
+    "Have a lot of filters been modified for the last time recently?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "af_timestamp\n",
+      "2009-03-31    29\n",
+      "2009-04-30    19\n",
+      "2009-05-31    11\n",
+      "2009-06-30     8\n",
+      "2009-07-31     8\n",
+      "2009-08-31    21\n",
+      "2009-09-30    21\n",
+      "2009-10-31     6\n",
+      "2009-11-30    13\n",
+      "2009-12-31     3\n",
+      "2010-01-31     9\n",
+      "2010-02-28     9\n",
+      "2010-03-31    10\n",
+      "2010-04-30     5\n",
+      "2010-05-31     5\n",
+      "2010-06-30     1\n",
+      "2010-07-31     0\n",
+      "2010-08-31    10\n",
+      "2010-09-30     5\n",
+      "2010-10-31     0\n",
+      "2010-11-30     4\n",
+      "2010-12-31     1\n",
+      "2011-01-31     2\n",
+      "2011-02-28     0\n",
+      "2011-03-31    23\n",
+      "2011-04-30     2\n",
+      "2011-05-31     3\n",
+      "2011-06-30     1\n",
+      "2011-07-31     0\n",
+      "2011-08-31     2\n",
+      "2011-09-30     1\n",
+      "2011-10-31     0\n",
+      "2011-11-30     1\n",
+      "2011-12-31     0\n",
+      "2012-01-31     0\n",
+      "2012-02-29    10\n",
+      "2012-03-31     0\n",
+      "2012-04-30     0\n",
+      "2012-05-31     0\n",
+      "2012-06-30     1\n",
+      "2012-07-31     1\n",
+      "2012-08-31    36\n",
+      "2012-09-30     2\n",
+      "2012-10-31     1\n",
+      "2012-11-30     1\n",
+      "2012-12-31     2\n",
+      "2013-01-31     4\n",
+      "2013-02-28     1\n",
+      "2013-03-31     2\n",
+      "2013-04-30    21\n",
+      "2013-05-31     0\n",
+      "2013-06-30     1\n",
+      "2013-07-31     2\n",
+      "2013-08-31     0\n",
+      "2013-09-30     0\n",
+      "2013-10-31     2\n",
+      "2013-11-30     0\n",
+      "2013-12-31     0\n",
+      "2014-01-31    13\n",
+      "2014-02-28     0\n",
+      "2014-03-31     9\n",
+      "2014-04-30     3\n",
+      "2014-05-31     0\n",
+      "2014-06-30     1\n",
+      "2014-07-31     0\n",
+      "2014-08-31     3\n",
+      "2014-09-30     1\n",
+      "2014-10-31     0\n",
+      "2014-11-30     0\n",
+      "2014-12-31     3\n",
+      "2015-01-31     4\n",
+      "2015-02-28    33\n",
+      "2015-03-31     2\n",
+      "2015-04-30     3\n",
+      "2015-05-31     2\n",
+      "2015-06-30    12\n",
+      "2015-07-31     9\n",
+      "2015-08-31     8\n",
+      "2015-09-30     2\n",
+      "2015-10-31     1\n",
+      "2015-11-30     3\n",
+      "2015-12-31     8\n",
+      "2016-01-31     5\n",
+      "2016-02-29     3\n",
+      "2016-03-31     2\n",
+      "2016-04-30     6\n",
+      "2016-05-31     2\n",
+      "2016-06-30    93\n",
+      "2016-07-31     8\n",
+      "2016-08-31    43\n",
+      "2016-09-30    21\n",
+      "2016-10-31     7\n",
+      "2016-11-30     5\n",
+      "2016-12-31     7\n",
+      "2017-01-31     9\n",
+      "2017-02-28     7\n",
+      "2017-03-31     6\n",
+      "2017-04-30    25\n",
+      "2017-05-31    30\n",
+      "2017-06-30     4\n",
+      "2017-07-31     3\n",
+      "2017-08-31     1\n",
+      "2017-09-30     4\n",
+      "2017-10-31    10\n",
+      "2017-11-30     3\n",
+      "2017-12-31     6\n",
+      "2018-01-31     6\n",
+      "2018-02-28     2\n",
+      "2018-03-31     7\n",
+      "2018-04-30    21\n",
+      "2018-05-31     3\n",
+      "2018-06-30     3\n",
+      "2018-07-31     9\n",
+      "2018-08-31     9\n",
+      "2018-09-30    11\n",
+      "2018-10-31    42\n",
+      "2018-11-30    34\n",
+      "2018-12-31    32\n",
+      "2019-01-31    25\n",
+      "Freq: M, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_origin['af_timestamp'] = pd.to_datetime(df_origin['af_timestamp'], format=\"%Y%m%d%H%M%S\")\n",
+    "\n",
+    "#df_modified = df_origin['af_timestamp'].groupby([df_origin.af_timestamp.dt.to_period(\"M\")]).agg('count')\n",
+    "#df_modified\n",
+    "df_modified = df_origin.set_index('af_timestamp').resample(\"M\").size()\n",
+    "\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    print (df_modified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Num filters')\n",
+    "plt.plot(df_modified, 'bo')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter hits over the years"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,