We propose an approach which computes a single aggregate score of an artifact that reflects the popularity across different social media sites and not just limited to any particular site.

1. Computing Social Score of
Web Artifacts
IRE Major Project
IIIT Hyderabad
Simran Kedia
Jaspreet Kaur
Soumyajit Ganguly
J N Venkatesh

2. Outline
● Introduction
● Motivation
● Challenges
● Problem Statement
● Approach
● Final Score
● Experiments
● Future Work
● Thank You!

3. Introduction
● Web artifacts are items like articles, blog posts, videos,
or any other URL.
● Various users post and share these artifacts on different
social media sites like Facebook, Twitter, etc.
● Each of the social media sites have their own metrics for
calculating the popularity score of artifacts posted.
● In this project, we propose an approach which computes
a single aggregate score of an artifact that reflects the
popularity across different social media sites and not
just limited to any particular site.

4. Motivation
● In todays world, opinion in Social Media matters!! Everyone of us
want to know how popular our web contents are!!
● The Popularity score calculator can be useful in the following cases:
o The Publicity Team of a company may want to know the
popularity score of its advertisement video across the social
media sites and see how people are reacting to it.
o Any content creator company which creates and shares its
content on different social media sites, will get to know the
popularity score and get feedback about the content.

5. Challenges
There are two main challenges to be tackled, in computing the
score:
● An artifact could have been shared by multiple people and on
multiple social sites. So, we have to aggregate all such shares
for calculating the score.
● There will be many posts and tweets where people discuss/talk
about an artifact, but they do not always quote the artifacts.
Ideally, these tweets/posts should also contribute to the
popularity of the artifact. But, matching these tweets/posts to
an artifact is quite a bit of challenge.

6. Problem Statement
Given datasets of Facebook and Twitter crawled data, we
would compute the popularity score of all the artifacts
present in the datasets.
We will compute this score by aggregating not only the
tweets/posts, which have quoted the artifacts but also the
tweets/posts which talk about the artifact, and assign a
combined score obtained for each of the artifacts.

7. Approach

8. Approach
● Gathering Data
o Used Twitter4j and Python Facebook sdk for crawling
twitter and facebook posts respectively.
● Parsing
o Used html2text tool for extracting the text from artifacts
● PreProcessing
o Performed Tokenization, Stop Word Removal, Stemming
● Algorithms - kNN, Indexing, kMeans
● Results

9. Approach - kNN
● Transformed the pre-processed data into vectors using tf-idf
normalization.
● Built a k Nearest Neighbour classifier considering all artifact
vectors as training data.
● For every non-artifact vectors, the top 10 closest artifacts are
computed with similarity score.
● Now, for every artifact in the results of kNN (for each non-artifact),
we update likes(favorites), shares(retweets), and comments count
(of artifact) as follows:
NewCount = OldCount + NormalizedSimilarityScore ∗
CountOfNonArtifact
The count refers to the like/share/comment count of an artifact. OldCount
refers to the original like/share/comment count of the artifacts

10. Approach - Indexing
● The extracted text from the artifacts is written to individual files
and placed in a folder.
● Used Lucene to create an index on this data.
● Treated all non-artifacts as a query and retrieved the top 10
matches for artifact texts along with ranking score.
● Now, for every artifact in the results(for each non-artifact query),
we update the likes(favorites), shares(retweets), and comments
count (of artifact) as follows:
NewCount = OldCount + NormalizedSimilarityScore ∗ CountOfNonArtifact
The count refers to the like/share/comment count of an artifact. OldCount
refers to the original like/share/comment count of the artifacts.

11. Final Score
● fb score
x * no_of_likes + y * no_of_comments + z * no_of_shares
● twitter score
x * no_of_favorite + y * no_of_comments + z * no_of_retweets
x, y, z are constants which act as weights for each of the associated
count. In our experiments we kept x=1, y=2 and z=5, thus giving
more weightage to shares.

12. Experiments
● For preparing dataset, we crawled all the Facebook and
Twitter pages related to the ICC Cricket World Cup.
● We got around 8770 unique artifacts from the crawled
data of Facebook and Twitter.
● We used Scikit-learn, a Python library for implementing
the kNN algorithm.
● We used Lucene for implementing our indexing
approach.
● We used Scikit-learn, a Python library for implementing
the k-means algorithm.

13. Experiments
● Choosing a good value of ’k’ was a difficult task.
● We tried with k as 100 and went up till k is 2500 and
recorded the silhouette score which measures the inter-
class and intra-class distances.
● So, we decided not to go ahead with this approach and
thus do not show its results in the future sections.
● More details in the report.

14. Result
● The output format will be a file containing (<artifact>, <social
score>) for each of the artifacts for both the methods.
● In both our methods, the artifact (shared by official ICC) about
Australia winning the world cup came out to be the top scorer.
● For both the approaches, top results that differed from each other
were within short distances (in ranks) from one another.
● A post about Srilankan cricketers retiring also got a high score from
both and was within top 5 artifacts.
● The famous “Mauka Mauka” ad from Star India could not come up
in Top 20, from either of the methods, may be due to its popularity
only in the Indian Sub-Continent.

15. Future Work
● We can extend the current approach and make it more
dynamic, as every second, hundreds of new tweets and posts
come up on Twitter and Facebook respectively.
● Better techniques for data crawling could be used. Presently,
there are some links, from which we could not crawl the
content, because of cookie issues.
● A decay factor can be used while calculating the social score
to ensure that the most relevant information influences the
computed popularity to an appropriate degree.
● A graph can be plotted which will depict, how popularity is
changing over the time.

16. Thank You!
● Demo of this project - http://goo.gl/p4ylrq
● Code+Report of this project - http://goo.gl/sWuZjm
● Any feedback/suggestions are welcome.
● You can contact us via eMail:
Venkatesh J N - jn.venkatesh@research.iiit.ac.in
Simran Kedia - simran.kedia@students.iiit.ac.in
Soumyajit Ganguly - soumyajit.ganguly@research.iiit.ac.in
Japsreet Kaur - jaspreet.kaur@students.iiit.ac.in