Awesome
ICWSM 2019 paper: "Measuring the Importance of User-Generated Content to Search Engines" by Vincent, Johnson, Sheehan, and Hecht
You-Geo-See, or the analysis code for "Measuring the Importance of User-Generated Content to Search Engines"
This is the analysis code for the paper. This code is archival at this point and unlikely to undergo active development. We may slightly re-organize the code/files before the conference, but otherwise this will likely remain mostly static.
The data collection code is a fork of SerpScrap by user ecoron and lives here: https://github.com/nickmvincent/SerpScrap.
The data used for analyses is checked into this repo, in dbs/. Some of the scripts also save a copy in csvs/ for even easier reading (in case you just want to check something out in a text editor quickly).
The analysis is in analysis.py and the code that runs experiments is in main.py (requires forked SerpScrap library).
As mentioned in the paper, you may also want to consider using a headless browser-based scraping software. For instance, see https://github.com/NikolaiT/se-scraper.
In addition to providing automated processing of key components of the SERP (e.g. the links, their position, and their destination) our software is also designed to save the raw HTML of the SERPs. This allowed for more detailed human verification, i.e. we make sure that the representation of each SERP in the database matches how a human interprets the SERP. This is an important feature, as there is no guarantee that SERPs will keep the same content format or styles, which the software uses to distinguish different types of results. Therefore, it is important that human validation is performed when exploring new queries or after substantial time has passed, even after the software has been extensively validated for other queries.
Notes for potential readers
Before doing any scraping, please consider reading through the following resources relating to algorthmic auditing:
https://en.wikipedia.org/wiki/Web_scraping
To Run all Analyses
python analysis.py --db dbs/population_weighted_40_all.db "dbs/2018-01-18_population_weighted_40_extra.db" --write_long
This will produce the longform importance_df from which Figure 1 is produced. You can also load it up to do other analyses.
To see counts of our qualitative codes (ugc, corporate, political, journalistic):
python qual_code.py --db dbs/population_weighted_40_all.db "dbs/2018-01-18_population_weighted_40_extra.db" --count
What's up with importance_df.csv? Why so large?
importance_df.csv has all the "general results" (i.e. not geographical comparisons) in pure long-form.
This means there is there is one row for every SERP we looked at, for every domain we observed, for every metric we looked at
If you check at analysis.py under the # ANCHOR: MELT comment, you can see how it's created.
The main metrics of interest are "domain_appears" (true or false), (a fraction), "domain_rank" (int), and "domain_count" (int). You can also compute "domain_frac" or "domain_maps" (mean average precisions) by editing analysis.py.
Each of these metrics is repeated for the FULL_PAGE and the TOP_THREE subset.
Furthermore, each row must be duplicated for each query category. We can verify the long-form df contains each link 6240 times, e.g. df.domain.value_counts().
How can I see the geographic comparisons?
The statistically significant comparisons are written to fisher_comparisons.csv or ttest_comparisons.csv. All UGC comparisons are in comparisons_df.csv. All comparisons are in all_tests_together.csv. You can also go through the output/ folders to find a particular comparison, or a comparison for a specific query.
You can also explore the comparisons more freely using some of the code in sb.py. This is just a really simple notebook-style exploration script meant to be edited.
Note that to get results after accounting for qualitative codes you would need to re-run analysis.py with --coded_metrics argument.
Note the code can also be edited to use McNemar's test which gives very similar results to Fisher's test.
Warnings relate to running comparisons
Don't run ALL comparisons for ALL databses - for instance running a high-income vs. low-income test using data from the urban-rural database. Use collect_comparisons.py to get a manageable summary of all the tests
Sanity Checking Analysis
There's a lot of data manipulating going in analysis.py and then plotters.py.
One easy way to sanity check some results is to manually inspect the serp_df.csv file that's written in the subdirectories of outputs/.
For instance, if we want to double check the fact that our "importance_plot" suggets that medical queries appear in about 45% of all first-page results, we can open serp_df.describe().csv in 2018-01-18_population_weighted_40_extra.db__med_sample_first_20/ and verify that indeed, Wikipedia appeared in 360/800 = 0.45 of all pages.
We might also want to check the SQL database directly:
We could use this (probably inefficient, but easy to understand) query to get the # of pages with 1 Wikipedia article:
select COUNT (DISTINCT link.serp_id) from serp INNER JOIN link on serp.id = link.serp_id where serp.category = 'trending' and link.domain = 'en.wikipedia.org' and link.link_type = 'results';
select COUNT (DISTINCT link.serp_id) from serp INNER JOIN link on serp.id = link.serp_id where serp.category = 'trending' and link.link_type = 'results';
for comparison
select COUNT (DISTINCT link.serp_id) from serp INNER JOIN link on serp.id = link.serp_id where serp.category = 'popular' and link.domain = 'en.wikipedia.org' and link.link_type = 'results';