Video Pollution on the Web

In the previous post I was writing about the long tail and the rise of niche culture. The democratization of production and distribution tools and integration of supply with demand led to the huge diversity of choice. Without filters, e.g. search engines, recommendations, blogs and user ratings, users can lost themselves in a multitude of products, because the long tail has a dynamic and wide range of quality.

Beside all the positive facts that new technologies brought, the impact of pollution on the web is becoming evident to society. In the last issue of First Monday, I found an interesting article called Video Pollution on the Web. Authors say that videos have become a predominant part of users' daily lives on the Web, especially with the emergence of video sharing services, such as YouTube. Part of the huge success of multimedia content in the Web is due to the change on the user perspective from content consumer to content creator. By allowing users to publicize their independently generated content, the long tail of videos is becoming longer but video sharing networks become susceptible to different types of pollution. As example, users can pollute the system spreading video messages containing undesirable content. Users can also associate metadata with videos in attempt to fool video search engines (i.e., popular tags, but unrelated to the content). Moreover, users can upload identical videos, generating duplicates of the same content on the system.

Authors identified following types of pollution:

• Redundant information (near-duplicates); since users can freely upload content in VSS systems, it is natural to expect that part of these videos is identical or very similar, this type pollute search results.
• Incorrect, noisy, imprecise, or manipulated meta–information (metadata pollution); one of the new trends of the Web 2.0 is to allow users to freely create and associate metadata to the content. The main problem caused by this type of pollution is related with content retrieval, as most information retrieval mechanisms rely mostly on metadata. If a video content is not well described by its meta–information, it can not be found in search results or appear in lists of related videos.
  
• Undesired or unsolicited information (video spam); in video sharing systems, videos can be used as objects of interactions between users. Similarly to e–mail spam, link spam, and spam in blogs and forums, conversations established in video social networks may contain video spam. Video messages can be considered spam if the video communication is unsolicited or is completely unrelated to the subject of the conversation or discussion.

Pollution can cause negative impacts on system aspects such as content distribution networks, and video search engines. The impact on the user caused by this type of content may include:

• loss of interest in using the system, as some of the retrieved information is mostly redundant or does not match well to an expressed information need or interest;
• distrust in the system, as some of the retrieved information is clearly not related to the current interest or navigation pattern, being perhaps offensive or being included only to take advantage of the user;
  
• impatience, as some resources are very difficult to find or due to the low performance of the system, among many others (Benevenuto et al. 2010).

Authors have also suggested pollution control strategies, which may be applied to all of the three types of video pollution, video duplicates, metadata pollution and video spam.

1. Automatic approach is widely used to control several types of pollution in Web environments and relies on machine learning algorithms (e.g., supervised classification). Basically, special purpose algorithms are developed to automatically detect and fight polluters and polluted contents.
   
2. Another filter or control strategy is feedback from users. By allowing users to flag a video when they encounter pollution, the system is able to remove some sort of undesired video objects.
   
3. The third is collaborative approach. Current popular video systems such as YouTube organize videos around online communities structured as personal (or “egocentric”) networks, with the individual at the center of their own community. One interesting approach to tackle pollution consists of empowering members of online communities with mechanisms to clean or report members that do not contribute positively to the community. This approach builds on the idea that each user should take care of the system and the content that other users upload, editing the content and associated metadata of every video uploaded to the system, like users do to articles in Wikipedia.
   
4. The fourth approach is “make life harder for polluters” by increasing the cost of sending spam or creating pollution (Benevenuto et al. 2010).

Beside all the positive examples that new technologies have brought for individual there are also issues that can deteriorate our experience with them. All the filters that authors are listing, are really important because a dynamic and wide range of quality of products and services in long tail of video sharing networks.

Resource:
Benevenuto, Fabricio, Tiago Rodrigues, Virgilio Almeida, Jussara Almeida, Marcos Goncalves, and Keith Ross. 2010. Video pollution on the Web. First Monday, vol.15, issue 4. Available at http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/issue/current.