This presentation on Diversification is part of the ARCOMEM training curriculum. Feel free to roam around or contact us on Twitter via @arcomem to learn more about ARCOMEM training on archiving Social Media.

1. Athena Research and Innovation Center
Yahoo! Research
Diversifying User Comments
on News Articles

2. 2
Problem
Problem description:
Given a news article and the respective set of user comments,
return a subset of the most diverse comments
Perception of a diverse set of comments:
A set of comments that represents different opinions and
sentiments,
…expressed by users with different demographic
characteristics,
…covering different aspects of the news article.
Motivation
Article’s content itself is not always enough to form a complete
view over a topic
The public opinion complements the article and represents
the “wisdom of the crowds”

3. 3
Example
Given a political article:
Find all the subtopics handled
 Persons related
 Events (election, bill voting)
Find all opinions and
sentiments expressed
 Positive/negative/neutral
 On the whole article/on specific
subtopics
Find different kinds of users
commenting
 Different demographics
 Different commenting history on
previous articles
Present a set of comments that
better represents the diversity
of the above dimensions

4. 4
Motivation
Several articles are very
popular (>10000 comments)
Articles get aggregated 
even more comments
Impossible for the reader to
review
Current comment sorting
options are based on more
simple criteria
Date
Votes
Replies

5. 5
Method outline
Define diversification criteria
Dimensions
 Content, Sentiment, Named Entities, User co-commenting
behavior
Define a (dis)similarity function that produces a diversity
score based on the criteria
Quantify the dissimilarity of comments
Weighted sum of cosine similarities on diversity feature
vectors
Apply and iterative heuristic algorithm that, at each step,
selects the candidate comment that maximizes a diversity
objective

6. 6
Method description - Criteria
Content
Baseline diversity criterion
Used in the rest of the literature to diversify search results.
Objective  obtain comments with diverse content.
Processing
 Comments’ text  term vectors
 Document length-normalized tf values

7. 7
Method description - Criteria
Named Entities (Nes)
Person, Organizations, Locations
Many times news articles revolve around Nes
 Even when an article talks about events or situations, usually one
or more Persons or Locations are involved
Objective  obtain comments that cover (uniformly) as many
different NEs as possible
Processing
 Extraction of Nes in comments (Stanford NER)
 Comments’ Nes  term vectors
 Document length-normalized tf values

8. 8
Method description - Criteria
Sentiment
9 classes of sentiment within the interval [-4, 4]
 -4  very negative
 4  very positive
 0  neutral
Expresses users’ opinions on the news articles’ topics.
Objective  obtain comments that cover (uniformly) different
classes of sentiment
Processing
 Sentiment analysis of the comment’s text (SentiStrength)
 Construct sentiment vectors
 Each vector value represents a sentiment class

9. 9
Comment scoring
Cosine similarity function between
A pair of comments
A comment and a set of comments
Apply the similarity function for each criterion separately
Produce a final diversity score as a weighted sum of all
criterion scores
Produce a final score that incorporates comment-to-article
similarity

10. 10
Algorithm (MAXSUM)
Initially
Empty diverse result set  all comments belong to the
candidate set
Arbitrary insertion of a candidate comment into the result set
Greedy construction heuristic
Compare each candidate comment with the centroid
(average) of the current result set
Finish after (k-1) iterations  k comments are inserted

11. 11
Evaluation
Comparison of methods’ coverage on different information
nuggets they contain
Baseline diversification based only on content
Proposed method (combination of multiple criteria)
Proposed methods outperform the baseline

12. 12
Framework - Implementations
A desktop java application retrieving news articles and
comments comments stored in a MySQL database
News and comments obtained by the NY Times API
Arcomem offline module for calculating diverse
WebObjects of WebResources