Soccer Team Popularity Data From Reddit
Contributed by Sharan Naribole. He is currently in the NYC Data Science Academy Online Bootcamp program taking place between December 2016 to April 2017. This blog is based on his bootcamp project - Python Web-scraping and Data Analysis.
Reddit Introduction and Project Goal
I am a big fan of soccer supporting the English club Manchester United F.C. for the last 15 years. Most of my soccer news updates and discussions occur on the website Reddit. Reddit, claiming to be "the front page of the internet", is a social news aggregation, web content rating, and discussion website, as described by Wikipedia. Different topics of interest are organized within their groups called subreddits. For example, the popular subreddits include technology, world news, pics, players like Cristiano Ronaldo and etc.
Reddit includes a subreddit for soccer content entries/r/soccer, comprising of an enormous community of over 500,000 subscribers. Hundreds of submissions (posts) are voted based on their content and discussed on a daily basis. The common submissions include live goals, news articles and pre-, live and post-match analysis.
Many time /r/soccer is where I vent my frustration whenever Manchester United suffers defeat! The anonymity of Reddit provides a powerful tool for users to express their opinions about the clubs they support and also the clubs they don't like. This unique feature provides a mechanism to prevent the building of echo chambers in the discussions. The figure below illustrates a snapshot of the front page of /r/soccer.
Fig 1. The front page of soccer subreddit.
/r/soccer also provides each subscriber the feature to select a "flair" which is the team crest (team logo) of the club/country the user supports. A miniature team logo is displayed beside the username in their posts and comments. This gives other readers context and insight into the user's thinking and adds another interesting dimension to the conversations that take place. Although one can observe the flairs for each users, Reddit does not provide the flair distribution across the whole of /r/soccer.
In this project, my objective is to scrape and analyze the flair distribution across the top posts in /r/soccer and this distribution's relationship with comments activity, submission score and submission type.
Fig 2. User flairs displayed beside their username in the comments.
For each submission, my goal is to collect the following features:
- Submission Title: to decipher the type of submission for e.g., goal video, news article, live match discussion etc.
- Submission Score (~= Upvotes - Downvotes): indicates the quality of the submission
- Number of comments: indicates the user activity on the submission
- Per-user flair map: dictionary of unique username and user flair in the top 500 comments. This will be utilized for the flair distribution analysis.
Currently, for web-scraping, there are a wide variety of Python packages available for webscraping including BeautifulSoup, Selenium and Scrapy etc. I used Scrapy because it provides a simple and structured framework to design a Spider for crawling multiple levels of pages on a website. For my project, there are two levels of crawling. The top level is where the different submission titles are listed along with their scores (Fig. 1) and the second level is the comments for each submission (Fig 2.). Later, I will describe how we can design the multi-level crawl.
The two main files in a Scrapy project directory are the items.py file and the spider crawl file. In items.py, we can define containers for storing the scraped information.
Next step is to define how our spider should crawl. I define a FlairSpider class provided with the starting url and crawling motion. To get sufficient data for the analysis, I collect the above features for each of top 1000 posts in the period Nov 12- Dec 12 2016.
For this purpose, I provide the start_url to extract the top posts in the past month. Reddit provides the top 1000 posts in the past month in a descending order of 25 posts per page. Additionally, I provide parameters for the comments constraint and upper limit on the comments extracted per submission. I placed a constraint that a submission should at least contain 100 comments so that I have a big enough sample size for flair distribution conditional on the submission. Over 500 out of the 1000 submissions met this constraint.
Using the start_url, the spider begins the crawl. The parse() function is the default method called to handle the response downloaded for each of links in the start_urls. First, I extract the links pointing to comments page for each submission. As illustrated in Fig. 3, I utilize the SelectorGadget tool to provide me the CSS selector only for the comments and ignore other CSS selectors.
Fig 3. SelectorGadget in action.
I filter the comment links based on the total number of comments as explained previously. If the number of comments of a submission exceeds the lower limit, the crawler stores the total number of comments in a new FlairsItem container. As we also want to extract user-flair map for each submission, a new request parse_submission() is made per submission with our partially filled FlairsItem container stored in the metadata. For this new request, I provide the url to the comments page of the submission sorted by the top 500 comments. These are typically the top comments any reader views on scrolling the comments page.
In the above request, I again utilize SelectorGadget to find out the CSS selectors for the submission title, score and username-flair mapping. I store the dictionary of unique users and their corresponding flairs as I am interesting in analyzing the flair diversity in the top comments per submission. Scrapy provides a command line argument to store the data in csv/json format for further processing.
The goal of data processing is to compute the following metrics for each submission:
- Flair diversity: the unique number of flairs
- Percentage share per flair: The percentage of comments belonging to a given flair. This metric is computed for every team both club and country flairs for e.g. England, Real Madrid, Brazil etc.
- Top Percentage share: The highest percentage value among all flairs
- Number of comments
The function compute_flair_stats() computes the percentage share per flair given the flair_map scraped for a submission using Scrapy. Using Python pandas library and above function, I fill two pandas dataframes, per_submission_metrics and per_flair_metrics that prepare the scraped data for analysis. Fig. 4 illustrates the structure of the two dataframes.
Fig 4. Illustration of the key DataFrame structures.
Flair Diversity Correlation
A user can participate in a submission either by a) upvoting/downvoting the submission consequently increasing/decreasing the submission score and/or b) a more involved participation by commenting on the submission. My hypothesis is that an average user's participation is higher in posts related to their flair (the team they support) in comparison to a generic post.
Fig 5. Flair Diversity Correlation.
Fig 5. illustrates a scatter plot between the number of comments and flair diversity with the size of the bubble proportional to the submission score.
Expectedly, the flair diversity increases with the number of comments. Also, with increase in diversity, even for low number of comments, the submission scores are much higher. These might be posts for which the quality of the content is enough to generate the high scores and increased flair diversity for e.g. world-class goal by a popular player, live match discussion between top teams Real Madrid and Barcelona. To analyze this hypothesis, next, I dive deeper into the relationship between the different metrics and the type of submission.
Submission Type Analysis
I classify the submissions into three high-level categories:
- Goal video submissions: Goal videos are posted in near real-time in the formats gfycat, streamable etc. My hypothesis is that goal submissions are expected to have a statistically higher flair diversity as the goals are discussed for their quality and not just the teams involved in the goal.
- Match Thread submissions: These submissions are automated submissions generated one hour before, during and immediately after a live soccer match. My hypothesis is that these posts receive lower flair diversity as users supporting the two teams taking part in the match are expected to have higher share of comments including the top ones. At the same time, because these threads are meant for discussion of various events during a match, they are expected to contain higher number of comments in comparison to goal submissions.
- Rest: All other types of submissions are grouped into this category.
I achieve the detection of Match Thread and Goal video submissions by applying the word_locate() and check_goal() functions respectively.
Fig 6. illustrates the distributions of the metrics based on the submission type.
Typically, the match thread discussions are mostly active during a live match and in its immediate aftermath. This results in the highest number of comments for Match Threads. In contrast, the submission score continues to rise many hours after the Goal submission is posted as they are rated on quality. Hence, the submission score is highest for goal submissions. We can observe even for the Rest category, there are quite a few outliers having a high submission score. These are typically news articles/tweets etc. transcending the interest of a particular section of soccer fans and appealing to a larger audience.
(a) Submission Score
(b) Number of Comments
(c) Flair Diversity
Fig 6. Submission type analysis.
Interestingly, my hypothesis that flair diversity for Goal submissions would be highest turned out to be false. The Match Threads edge the other two categories. This is because we have particularly selected the top 1000 submissions for analysis and not a random sampling of 1000 submissions in the same duration. Because the Match Threads are among the top posts, these are submissions for matches watched by a wider audience in comparison to an average match. I expect this result to not to be true if we randomly selected the submissions.
Last, I analyze the distributions of the percentage share of the different flairs across all the submissions. We observe that the leaderboard is dominated by the English Premier League clubs followed by the two Spanish giants, Real Madrid and Barcelona. Reddit being a predominantly English-speaking website and English Premier League being the most popular league in the world, this result is expected.
There are several directions in which my framework can be expanded. Few of them include:
- Sentiment Analysis: Perform team-specific sentiment analysis of the posts over the period of a season ( year). An interesting case study would be Leicester City, a lower-ranked team that went on to win the English Premier League 2015-2016 season for the first time in their 132-year history.
- Topic Modeling: Apply topic modeling for deeper classification of the submissions
In this project, I have scraped data from Reddit's soccer subreddit to analyze the user flair distribution across submissions and its correlation with Submission Score and comments activity. My results show that the metrics vary significantly based on the type of submissions and validate the popularity of English Premier League clubs in the subreddit.
The code for the project can be found here.