diff --git a/source/topic_model_visualization.rst b/source/topic_model_visualization.rst
index b26b247..a46bf59 100644
--- a/source/topic_model_visualization.rst
+++ b/source/topic_model_visualization.rst
@@ -378,7 +378,7 @@ words.  We do this by calculating the proportion of words assigned to topic
 turn will turn our matrix of word-topic counts into a matrix of word-topic
 proportions. For example, a value of 0.5 in the matrix at row 5 and column
 0 indicates that the specified word type (``mallet_vocab[5]``) accounts for 50
-  percent of all words assigned to topic 0.
+percent of all words assigned to topic 0.
 
 .. ipython:: python
 
diff --git a/source/visualizing_trends.rst b/source/visualizing_trends.rst
index a13c6a8..4570660 100644
--- a/source/visualizing_trends.rst
+++ b/source/visualizing_trends.rst
@@ -33,8 +33,8 @@ revolutionary activity in *Les Misérables*:
 revolutionary *Les Amis de l'ABC*.)
 
 .. note:: Probabilistic models such as topic models often benefit from
-incorporating information about where an individual text falls in a larger
-sequence of texts :cite:`blei_dynamic_2006`.
+    incorporating information about where an individual text falls in a larger
+    sequence of texts :cite:`blei_dynamic_2006`.
 
 
 Plotting trends