assistant: There have been various attempts to identify and address misinformation and propaganda on contemporary social media platforms, some of the techniques that have been used include:

- Identifying clusters in the social graph, representing cliques, social groups, and botnets, and then classifying the nature of the information and interaction that characterizes each cluster. 
- Tracking patterns of account creation and identifying periods of time with unusual activity.
- Identifying information that is reposted without attribution within platforms, noting that posters are connected, and then classifying how and if they were modified.

Once information and users are classified by these, or similar techniques, it may be prudent to identify which classes of users and information are behaving in a:
- Positive way, ie, increasing the veracity of information, adding additional context, spreading information after verifying and discussing it, and debunking or addressing content that does not meet these criteria. 
- A neutral way, ie, spreading information and content without verification or based on social pressures, simplifying complex situations and removing context, modifying content in a benign or humorous way without altering it's meaning, or amplifying drama or conflict without debunking or verifying any position.
- A negative way, ie, spreading information and content that benefits a specific agenda, regardless of it's veracity, removing context or fraudulently modifying information or content to support a specific position, amplifying drama and conflict in a way that specifically targets groups that oppose the viewpoints you hold.

It should be noted that by first dividing users and content into clusters and then breaking the classifications of those clusters down in this way, we significantly reduce the amount of information we need to retrieve from outside of the social graphs we have access to. 

For quantifying things like "the veracity of information" and "fraudulently modified information" we need to retrieve information from external sources to help classify content. This is a difficult problem, and will likely need trusted human researchers to manually look into the sources of claims, their historical reliability, and any underlying interests and incentives those sources have, in order to verify that information. A good summary of a repeatable process for evaluating unfamiliar sources could be to:

 - Identify however many sources make a similar claim to the one you are verifying, and try to identify any primary sources among them.
 - Look into the history of these primary sources, have they made any claims that you can independently verify? If not, is that due to the difficulty of verifying the claim, or is it due to the unverifiability of the claim? 
 - Look into the connections and affiliations of these sources, do they have an explicit incentive to make their claims? If they do, are there any counterclaims elsewhere that can also be verified? 

However, since in most real world examples of propaganda and misinformation, false and unverified information is only used for shock value at the "entry" level of a disinformation pipeline, and the ultimate goal is usually to create a more permanent seperation between the ingroup and the outgroup, the quality of those quantifications can help our classifications, but may not be critical for them. Instead, we can classify most misinformation and disinformation solely based on one or two verified points of fact and then classify clusters of users that actively and reliably behave negatively towards verified information as sources of misinformation (modified or false information,) or propaganda (not necessarily modified information, but a filtering of reality to support a specific position or viewpoint over the whole truth). 

There are likely gaps in this scheme that could lead to it being gamed or manipulated to censor or harm benign or dissident minority activities, so it is important to seek the aid of misinformation or harm reduction experts before you use the outputs of this scheme to enact any action or create any policy.

assistant: This is a complex issue as the world, and its events often have many competing interpretations.  One of the key issues is the time to research the background and identify the source of each piece of news is prohibitive.   It may be possible to automate some of this process with AI assistance, doing fact checks and looking for trends across many media platforms.  The news and the associated background behind it could then be presented to give a more objective picture of events.

assistant: Your first prompt is very broad with several questions that each deserve an extensive response. Limiting a prompt to a single question or task makes it easier to give a good response.

To answer your first question: there are questions you can ask to validate trustworthiness, but they depends on who you ask them. If you meant it as questions you should ask yourself when considering the trustworthiness of a news article, then a good question would be "who published it and what are their affiliations?" Another question could be "can I spot any expressions, opinions, and dogwhistles that are often used in misinformation or propaganda?"

You should definitely factor in the nature of the platform and its user base. As you mentioned, platforms like 4chan and Twitter are know to be toxic, with high activity of trolls and bots. This makes it more likely that information found on those platforms originates from dishonest sources.

I don't know if a system like Quora is enough to repel misinformation and propaganda. It is hard to estimate whether self-proclaimed experts are trustworthy.

Reading scientific publications is an excellent way to gain knowledge that is more trustworthy, as a good publication clearly states how the information was collected and how the results were interpreted. It is still important to check the quality of the article and the source of funding, as the quality of scientific research varies greatly and corporate-funded science is know to favor results that match the interests of the company providing the funding. Arxiv is a good and open source, but keep in mind that they host preprint papers, which are papers that have not yet been peer reviewed and published. As such, they have been subject to less quality control than publications by renowned journals.

prompter: Thank you for your reply, but I want to know how can I know if something I see on the interner is actually true.
Can you help me with that?