Datasets:
Tasks:
Text Retrieval
Modalities:
Image
Formats:
imagefolder
Sub-tasks:
document-retrieval
Languages:
English
Size:
< 1K
License:
annotations_creators: | |
- expert-generated | |
language: | |
- en | |
language_creators: | |
- found | |
license: | |
- cc-by-nc-sa-4.0 | |
multilinguality: | |
- monolingual | |
pretty_name: fcc-comments | |
size_categories: | |
- 10M<n<100M | |
source_datasets: | |
- original | |
tags: | |
- notice and comment | |
- regulation | |
- government | |
task_categories: | |
- text-retrieval | |
task_ids: | |
- document-retrieval | |
# Dataset Card for fcc-comments | |
## Table of Contents | |
- [Table of Contents](#table-of-contents) | |
- [Dataset Description](#dataset-description) | |
- [Dataset Summary](#dataset-summary) | |
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
- [Languages](#languages) | |
- [Dataset Structure](#dataset-structure) | |
- [Data Instances](#data-instances) | |
- [Data Fields](#data-fields) | |
- [Data Splits](#data-splits) | |
- [Dataset Creation](#dataset-creation) | |
- [Curation Rationale](#curation-rationale) | |
- [Source Data](#source-data) | |
- [Annotations](#annotations) | |
- [Personal and Sensitive Information](#personal-and-sensitive-information) | |
- [Considerations for Using the Data](#considerations-for-using-the-data) | |
- [Social Impact of Dataset](#social-impact-of-dataset) | |
- [Discussion of Biases](#discussion-of-biases) | |
- [Other Known Limitations](#other-known-limitations) | |
- [Additional Information](#additional-information) | |
- [Dataset Curators](#dataset-curators) | |
- [Licensing Information](#licensing-information) | |
- [Citation Information](#citation-information) | |
- [Contributions](#contributions) | |
## Dataset Description | |
- **Repository: https://github.com/slnader/fcc-comments ** | |
- **Paper: https://doi.org/10.1002/poi3.327 ** | |
### Dataset Summary | |
Online comment floods during public consultations have posed unique governance challenges for | |
regulatory bodies seeking relevant information on proposed regulations. | |
How should regulatory bodies separate spam and fake comments from genuine submissions by the public, | |
especially when fake comments are designed to imitate ordinary citizens? How can regulatory bodies | |
achieve both breadth and depth in their citations to the comment corpus? What is the best way to | |
select comments that represent the average submission and comments that supply highly specialized | |
information? | |
`fcc-comments` is an annotated version of the comment corpus from the Federal Communications Commission's | |
(FCC) 2017 "Restoring Internet Freedom" proceeding. The source data were downloaded directly from the FCC's Electronic | |
Comment Filing System (ECFS) between January and February of 2019 and include raw comment text and metadata on | |
comment submissions. The comment data were processed to be in a consistent format | |
(machine-readable pdf or plain text), and annotated with three types of information: whether the comment was cited in the | |
agency's final order, the type of commenter (individual, interest group, business group), and whether the comment was associated with an in-person meeting. | |
The release also includes query-term and document-term matrices to facilitate keyword searches on the comment corpus. | |
An example of how these can be used with the bm25 algorithm can be found | |
[here](https://github.com/slnader/fcc-comments/blob/main/process_comments/1_score_comments.py). | |
## Dataset Structure | |
FCC relational database (fcc.pgsql): The core components of the database include a table for submission metadata, | |
a table for attachment metadata, a table for filer metadata, and a table that contains comment text if submitted in express format. | |
In addition to these core tables, there are several derived tables specific to the analyses in the paper, | |
including which submissions and attachments were cited in the final order, which submissions were associated with in-person meetings, | |
and which submissions were associated with interest groups. Full documentation of the tables can be found in fcc_database.md. | |
Attachments (attachments.tar.gz): Attachments to submissions that could be converted to text via OCR and saved in machine-readable pdf format. | |
The filenames are formatted as [submission_id]_[document_id].pdf, where submission_id and document_id are keys in the relational database. | |
Search datasets (search.tar.gz): Objects to facilitate prototyping of search algorithms on the comment corpus. Contains the following elements: | |
| Filename | description | | |
| ----------- | ----------- | | |
query_dtm.pickle | Query-term matrix (79x3986) in sparse csr format (rows are queries, columns are bigram keyword counts). | |
query_text.pickle | Dictionary keyed by the paragraph number in the FCC’s Notice of Proposed Rulemaking. Values are the text of the query containing a call for comments. | | |
search_dtms_express.pickle | Document-term matrix for express comments (3800691x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). | | |
search_index_express.pickle | Pandas dataframe containing unique id and total term length for express comments. | | |
search_dtms.pickle | Document-term matrix for standard comment attachments (44655x3986) in sparse csr format (rows are comment pages, columns are bigram keyword counts). | | |
search_index.pickle | Pandas dataframe containing unique id and total term length for standard comment attachments. | | |
### Data Fields | |
The following tables are available in fcc.pgsql: | |
- comments: plain text comments associated with submissions | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
| comment_id | character varying(64) | unique id for plain text comment | | |
comment_text | text | raw text of plain text comment | |
row_id | integer | row sequence for plain text comments | |
- submissions: metadata for submissions | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
submission_id | character varying(20) | unique id for submission | |
submission_type | character varying(100) | type of submission (e.g., comment, reply, statement) | |
express_comment | numeric | 1 if express comment | |
date_received | date | date submission was received | |
contact_email | character varying(255) | submitter email address | |
city | character varying(255) | submitter city | |
address_line_1 | character varying(255) | submitter address line 1 | |
address_line_2 | character varying(255) | submitter address line 2 | |
state | character varying(255) | submitter state | |
zip_code | character varying(50) | submitter zip | |
comment_id | character varying(64) | unique id for plain text comment | |
- filers: names of filers associated with submissions | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
submission_id | character varying(20) | unique id for submission | |
filer_name | character varying(250) | name of filer associated with submission | |
- documents: attachments associated with submissions | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
submission_id | character varying(20) | unique id for submission | |
document_name | text | filename of attachment | |
download_status | numeric | status of attachment download | |
document_id | character varying(64) | unique id for attachment | |
file_extension | character varying(4) | file extension for attachment | |
- filers_cited: citations from final order | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
point | numeric | paragraph number in final order | |
filer_name | character varying(250) | name of cited filer | |
submission_type | character varying(12) | type of submission as indicated in final order | |
page_numbers | text[] | cited page numbers | |
cite_id | integer | unique id for citation | |
filer_id | character varying(250) | id for cited filer | |
- docs_cited: attachments associated with cited submissions | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
cite_id | numeric | unique id for citation | |
submission_id | character varying(20) | unique id for submission | |
document_id | character varying(64) | unique id for attachment | |
- near_duplicates: lookup table for comment near-duplicates | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
target_document_id | unique id for target document | |
duplicate_document_id | unique id for duplicate of target document | |
- exact_duplicates: lookup table for comment exact duplicates | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
target_document_id | character varying(100) | unique id for target document | |
duplicate_document_id | character varying(100) | unique id for duplicate of target document | |
- in_person_exparte: submissions associated with ex parte meeting | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
submission_id | character varying(20) | unique id for submission | |
- interest_groups: submissions associated with interest groups | |
| column | type | description | | |
| ----------- | ----------- | ----------- | | |
submission_id | character varying(20) | unique id for submission | |
business | numeric | 1 if business group, 0 otherwise | |
## Dataset Creation | |
### Curation Rationale | |
The data were curated to perform information retrieval and summarization tasks as documented in https://doi.org/10.1002/poi3.327. | |
### Source Data | |
#### Initial Data Collection and Normalization | |
The data for this study come from the FCC's Electronic Comment Filing System (ECFS) system, accessed between January and February of 2019. | |
I converted the API responses into a normalized, relational database containing information on 23,951,967 submissions. | |
23,938,686 "express" submissions contained a single plain text comment submitted directly through the comment form. | |
13,821 "standard" submissions contained one or more comment documents submitted as attachments in various file formats. | |
While the FCC permitted any file format for attachments, I only consider documents attached in pdf, plain text, rich text, | |
and Microsoft Word file formats, and I drop submitted documents that were simply copies of the FCC’s official documents (e.g., the NPRM itself). | |
Using standard OCR software, I attempted to convert all attachments into plain text and saved them as machine-readable pdfs. | |
#### Who are the source language producers? | |
All submitters of public comments during the public comment period (but see note on fake comments in considerations). | |
### Annotations | |
#### Annotation process | |
- Citations: I consider citations from the main text of the FCC's final rule. I did not include citations to | |
supporting documents not available through ECFS (e.g., court decisions), nor did I include citations | |
to submissions from prior FCC proceedings. The direct citations to filed submissions are included | |
in a series of 1,186 footnotes. The FCC’s citation format typically followed a relatively standard | |
pattern: the name of the filer (e.g., Verizon), a description of the document (e.g., Comment), and | |
at times a page number. I extracted citations from the text using regular expressions. Based on a | |
random sample of paragraphs from the final order, the regular expressions identified 98% of eligible citations, | |
while successfully excluding all non-citation text. In total, this produced 1,886 unique citations. | |
I then identified which of the comments were cited. First, I identified all documents from the cited filer | |
that had enough pages to contain the page number cited (if provided), and, where applicable, whose filename | |
contained the moniker from the FCC’s citation (e.g., "Reply"). The majority of citations matched to only one | |
possible comment submitted, and I identified the re- maining cited comments through manual review of the citations. | |
In this way, I was able to tag documents associated with all but three citations. When the same cited document was | |
submitted under multiple separate submissions, I tagged all versions of the document as being cited. | |
- Commenter type: Comments are labeled as mass comments if 10 or more duplicate or near-duplicate copies were | |
submitted by individual commenters. Near-duplicates were defined as comments with non-zero identical information scores. | |
To identify the type of commenter for non-mass comments, I take advantage of the fact that the vast majority of organized | |
groups preferred standard submissions over express submissions. Any non-mass comment submitted as an express comment was | |
coded as coming from an individual. To distinguish between individuals and organizations that used standard submissions, | |
I use a first name and surname database from the names dataset Python package to characterize filer names as belonging to | |
individuals or organizations. I also use the domain of the submitter’s email address to re-categorize comments as coming | |
from organizations if they were submitted on behalf of organizations by an individual. Government officials were identified by | |
their .gov email addresses. I manually review this procedure for mischaracterizations. After obtaining a list of organization | |
names, I manually code each one as belonging to a business group or a non-business group. Government officials writing in | |
their official capacity were categorized as a non-business group. | |
- In-person meetings: To identify which commenters held in-person meetings with the agency, I collect all comments labeled | |
as an ex-parte submission in the EFCS. I manually review these submissions for mention of an in-person meeting. I label | |
a commenter as having held an in-person meeting if they submitted at least one ex-parte document that mentioned an in-person meeting. | |
#### Who are the annotators? | |
Annotations are a combination of automated and manual review done by the author. | |
### Personal and Sensitive Information | |
This dataset may contain personal and sensitive information, as there were no restrictions on what commenters could submit to | |
the agency. This dataset also contains numerous examples of profanity and spam. These comments represent what the FCC decided was | |
appropriate to share publicly on their own website. | |
## Considerations for Using the Data | |
### Discussion of Biases | |
This proceeding was famous for the large number of "fake" comments (comments impersonating ordinary citizens) submitted to the | |
agency (see [this report](https://ag.ny.gov/sites/default/files/oag-fakecommentsreport.pdf) by the NY AG for more information). | |
As such, this comment corpus contains a mix of computer-generated and natural language, and there is currently no way to reliably separate | |
mass comments submitted with the approval of the commenter and those submitted on behalf of the commenter without their knowledge. | |
## Additional Information | |
### Licensing Information | |
CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International. | |
### Citation Information | |
``` | |
@article{handan2022, | |
title={Do fake online comments pose a threat to regulatory policymaking? Evidence from Internet regulation in the United States}, | |
author={Handan-Nader, Cassandra}, | |
journal={Policy \& Internet}, | |
year={2022} | |
} | |
``` |