# Protection from 'Fake News': The Need for Descriptive Factual Labeling for Online Content
## 4 Risks, Challenges and Limitations
### Risks and Challenges  
First, the potential risks and challenges that may arise from labelling online news content are discussed. The first potential challenge is content providers or others 'gaming the system'. Malicious actors could target a specific article to promote using a botnet, and artificially enhance its credibility and other metrics. This poses a real risk to the effectiveness of an online labelling system and could cause users to trust manipulative content. A content labelling system could be combined with or work cooperatively with an automatic fake news or botnet detection system to potentially avoid this. A challenge that might occur with an automated, manual, or hybrid labelling system is false positives and false negatives, either due to human or automated system error. In either case, the user would be given bad information regarding the content and may lose trust in the labelling system. This could cause them to disregard its recommendations and warnings or opt out of its use. To mitigate this, a system of checks could be used. Either a human or machine learning-based review process could be used to correct falsely labelled articles. A third challenge is the profit motive from advertising on deceptive content sites. This was noted by [PERSON] and [PERSON] [14]. This profit incentive poses a direct threat to online consumer protection. Government oversight could mitigate this risk. A fourth challenge is news media sources rejecting information labels and disclaimers. News content creators are motivated to appeal to as many readers as possible. Thus, a content labelling system could be perceived as a financial threat. A balance that involves some sensational news may be required for outlets to remain competitive. A cooperativerelationship with news content providers is desirable to facilitate implementation. Ideally, any labelling system that is developed would be one that news providers are happy to incorporate. Another challenge is identifying what information would be relevant, useful, and accessible to the average consumer. A nutrition facts analogy exists. Some labeling does not relate to health but rather to other purchasing decision factors such as not containing GMO ingredients, which has no scientific consensus with regard to food safety [72]. Even if appropriate information is identified, promotion will likely be required to drive consumer use as both knowledge and motivation will be required, as is the case with food nutrition facts [19]. Once consumer knowledge deficiencies are identified, the development of knowledge and motivation can be driven through similar educational initiatives as have been used for food labeling. If a social media platform introduced labeling, it may raise questions regarding the platform's neutrality and impair the protections that this provides. In the United States, Section 230 of the 1996 Communications Decency Act states [73] that \"no provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider\". This protects social media companies from liability for what its users say on the platform. Similar legislation in other nations includes the Defamation Act 2013 in the United Kingdom [74] and Directive 2000/13/EC in the European Union [75]. Not every nation has similar law in place. Political pressure, new legislation, or judicial decisions may cause social media platforms to adjust their labeling policies. Finally, online content labelling could be perceived by some as being an infringement against Americans' First Amendment rights and similar rights in other countries. This may be a key reason for government restraint, thus far, in regulating online news content. Beyond legal questions, implementing this type of system presents considerable logistical challenges. In order to be effective, the system must be able to accurately classify most articles at launch. However, automatic classification requires exposure to numerous types of content and feedback.