Describe methods of framing and reframing public discourse by bots, analyzing examples of successful and failed narratives spread through bot networks.

Framing and reframing public discourse by bots involves strategically presenting information and narratives in a way that influences how the public understands and interprets events, issues, or ideas. Bots achieve this by selecting certain aspects of a story to emphasize while downplaying or omitting others, thereby shaping the narrative to align with a specific agenda. Framing is the initial act of creating a particular perspective or lens through which an issue is viewed. Reframing, on the other hand, is about changing or shifting an already established understanding of an issue. Both of these tactics are used by bot networks to control the narrative and manipulate public opinion.

One of the main methods of framing is by using carefully selected language and keywords. For example, if a bot network is trying to promote a particular political stance on environmental regulations, it might consistently use terms like "job-killing regulations" to frame the issue as one that pits the environment against the economy. Alternatively, a network supporting stricter regulations might use phrases like "environmental protections" to position the regulations as necessary for the health and well-being of the community. The bot network would repeatedly use this kind of framing in its posts and comments, pushing a specific perspective and influencing the understanding of the public, to adopt a specific viewpoint or position. The repetition of certain keywords or phrases will also reinforce the message and the specific framing.

Another effective method of framing involves selectively sharing evidence and data. Bots can use statistics, news articles, or quotes that support a specific narrative while conveniently ignoring information that contradicts it. For instance, a network trying to undermine confidence in a vaccine might repeatedly share studies that highlight negative side effects while ignoring or downplaying the overwhelming evidence of its safety and efficacy. By selectively choosing what to share, and by deliberately omitting data that goes against their narrative, they are able to shape public opinion about the vaccine. The repeated and persistent messaging on social media is an effective method for influencing users to believe a narrative even if it is not based on facts.

Reframing often involves challenging and countering an existing narrative, and presenting an alternative perspective. If a specific narrative has already been established by a media outlet or through public discussion, bots might try to reframe the debate by highlighting different aspects of the issue. For example, if a social movement is framed by some news media sources as a source of violence, a bot network might reframe it as a peaceful protest, by sharing videos or posts that show that side of the story, thus challenging the established narrative. Similarly, if a company is facing criticism for its environmental impact, the bot network might reframe the discussion to focus on positive actions taken by the company, by using its posts and comments to emphasize all the positive steps that are being taken by the company to minimize its environmental footprint. This is a method for taking the focus away from the negativity by introducing a counter-narrative.

Real-world examples of successful and failed narratives spread through bot networks highlight the power of framing and reframing. A successful example is how bot networks were used to amplify misinformation during elections, where bots repeated false or misleading information to influence voter perception. By consistently posting content that framed the candidates negatively and highlighted specific controversial issues, bot networks were able to successfully sway voter opinion. This shows how a successful framing campaign, combined with the repetition of misleading content can have a profound impact on public perception. On the other hand, examples of failed narratives include attempts to promote conspiracy theories that were immediately debunked by independent fact-checkers. For example, if a bot network tries to promote a conspiracy theory about an event that has an official explanation, and there are fact checkers who immediately debunk the conspiracy theory, it would be seen as a failed campaign. The fact-checking undermines the credibility of the message, and thereby reduces its effectiveness.

Another method of reframing is by labeling a particular viewpoint or action as something negative or controversial. For instance, if a bot network aims to undermine a social movement, it can label it as "radical" or "extremist," thereby discrediting it. Similarly, if the goal is to promote a particular political figure, that same bot network might call his opponent a "liar" or "corrupt." This process of labeling the opposition or the alternative is another effective way of controlling the framing. This labeling will also be repeatedly shared and amplified by the bot network, thus becoming part of the language of the campaign. The key is to make the narrative stick and for the message to be understood within that specific framing.

In summary, bots use a range of methods, including strategic use of language, selectively sharing information, challenging established narratives, and labeling alternative perspectives, to control public perception. Successful campaigns carefully craft narratives to tap into existing public opinions and beliefs, while also constantly monitoring the response to their messaging. Failed narratives often result from a lack of consistency in messaging, being unable to counter opposing arguments or when there is a strong level of factual evidence that shows the framing or reframing is not based on facts or evidence. The effective use of framing and reframing techniques by bot networks has a significant impact on public understanding of complex issues, highlighting the profound impact that bot networks can have on shaping public opinion.