Ibosiola et al. (2019)
Contents
Source Details
Ibosiola et al. (2019a) | |
Title: | Who Watches the Watchmen: Exploring Complaints on the Web |
Author(s): | Ibosiola, D., Castro, I., Stringhini, G., Uhlig, S., Tyson, G. |
Year: | 2019 |
Citation: | Ibosiola, D., Castro, I., Stringhini, G., Uhlig, S. And Tyson, G. (2019) Who Watches the Watchmen: Exploring Complaints on the Web. Proceedings of the 2019 World Wide Web Conference (WWW ’19), May13–17, 2019, San Francisco, CA, USA.ACM, New York, NY, USA, 10 pages.https://doi.org/10.1145/3308558.3313438 |
Link(s): | Open Access |
Key Related Studies: | |
Discipline: | |
Linked by: | Ibosiola et al. (2019b) |
About the Data | |
Data Description: | The study gathered details on complaints issued to websites using transparency repots from Google, Twitter, Bing and Vimeo, as well as the Lumen database. In total, they gathered over one billion URL complaints from nearly 40,000 notice senders. Metadata was also extracted to account for categories of work (e.g. blogs, games etc.) and liveness checks for the website. |
Data Type: | Primary and Secondary data |
Secondary Data Sources: | |
Data Collection Methods: | |
Data Analysis Methods: | |
Industry(ies): | |
Country(ies): | |
Cross Country Study?: | No |
Comparative Study?: | No |
Literature review?: | No |
Government or policy study?: | No |
Time Period(s) of Collection: |
|
Funder(s): |
Abstract
“Under increasing scrutiny, many web companies now offer bespoke mechanisms allowing any third party to file complaints (e.g., requesting the de-listing of a URL from a search engine). While this self-regulation might be a valuable web governance tool, it places huge responsibility within the hands of these organisations that demands close examination. We present the first large-scale study of web complaints (over 1 billion URLs). We find a range of complainants, largely focused on copyright enforcement. Whereas the majority of organisations are occasional users of the complaint system, we find a number of bulk senders specialised in targeting specific types of domain. We identify a series of trends and patterns amongst both the domains and complainants. By inspecting the availability of the domains, we also observe that a sizeable portion go offline shortly after complaints are generated. This paper sheds critical light on how complaints are issued, who they pertain to and which domains go offline after complaints are issued.”
Main Results of the Study
DMCA notices make up 98.6% of website complaints, significantly more than complaints regarding defamation, court orders or government requests.The study finds that complaints are highly skewed towards a few very active senders - the top ten complainants generate 41% of all notices. These complainants tend to be either large and influential copyright owners (such as British Phonographic Industry, Apdif Brasil, Apdif Mexico, Fox) or specialist third parties primarily targeting pirated content (Muso, Aiplex, Mark Monitor, Entura, Rivendell). “Bursts” of complaints are also apparent, noting that sender Idreto generated 350,000 notices in one day despite otherwise being an irregular sender.The data also demonstrates that complainants work strategically, often targeting specific categories of work. For example, 42% of MG Premium’s complaints targeted adult websites.Lastly, the authors note that the complaints process is seemingly effective - 22% of URLs are inaccessible within 4 weeks of the researchers having observed the complaint. Third-party enforcement agencies appear to have the most success in URL removal, with 53% of URLs detailed in complaints from Rivendell being removed within a week.
Policy Implications as Stated By Author
The authors highlight that the complaints system is open to misuse, noting patterns of bulk, repeat notices from senders, and websites with apparently auto-generated URLs spread across multiple domains. To streamline this process, the authors recommend (i) continued use of transparency points to understand complainants behaviours, and (ii) automation of the complaints process to filter out “spam” notices (cautioning that many complaints will be legitimate).
Coverage of Study
Datasets
{{{Dataset}}}