This is an example of how online authenticity is often tied to experience rather than physical identity. While @Sciencing_Bi was an anonymous identity, it was still an authentic identity to many academics in that it shared experiences of discrimination.

In this section, it is clearly explained why authenticity is important and its relationship to trust and emotions. What caught my attention is the fact that being “duped” is not simply being wrong; it is also a sense of being manipulated. This is why people react very negatively to things they consider as “fake” on the web.

What is interesting is that IRC already had a concept of “rooms” that were topic-based, and this is something that is still found today in services like Discord and Slack. What this shows is that having a community organized around shared interests has been a part of social media for a long time.

This aspect underscores the amount of work that went into being present on the internet during the Web 1.0 era. In contrast to the current social media platforms, people had to have technical skills to create their own web pages.

This note illustrates how conventions in language influence our perception of concepts of ethics. In pointing out that “ethics” is usually a plural noun, it is important to recognize that ethics is a system of thinking or a framework rather than a set of several distinct principles. In regard to words like “data” or “media,” it is evident that language is a product of society that is not bound by its original roots in Latin. Rather, it is important to focus on consistency in a given situation rather than a standard form. In regard to ethics, it is important to focus on understanding rather than simply applying a set of principles. In short, it is important to recognize that ethics is not simply a consideration of principles, but rather a consideration of language.

This is a timeline of how a single tweet on a social media platform can quickly go viral. Justine Sacco’s tweet started off reaching a very small audience, but once it was shared on a larger media platform, it became decoupled from its original context and quickly spread. The additional factor that increased public speculation and participation was the unavailability of Justine Sacco during the flight of her plane, which turned the event into a real-time spectacle. The popular hashtag on the trending page illustrates the way online platforms promote a sense of collective surveillance and judgment where viewers are not only responding to the content but are also responding to the responses of other viewers.

The difference between data and metadata made it clear how much information there was outside of the actual data itself. Although it appears as though the pictures and texts contain the ‘main’ data, the metadata (engagement statistics, timing, etc.) could potentially be worth much more to platforms and researchers alike. The way that metadata can show patterns of behavior with little analysis necessary on the actual data is fascinating.

On the surface, this tweet appears to be purely innocent and cute, but in fact, there is quite a bit of information that can be gleaned from this tweet. Information such as the level of engagement, the posting of the tweet, images included in the tweet, and even the tone of the tweet can be analyzed for information about the type of tweet that works best.

This section has been an eye-opener for me regarding the potential for bots to deliberately influence public perception rather than just being an annoyance to users with spam messages. The case of astroturfing illustrates the potential for fake crowds to make the opinion of a few appear to be the more popular view than others.

This definition of a bot caught my attention because it emphasizes the actions that the program undertakes, rather than whether these actions are carried out for good or bad reasons. Another point that caught my attention was that this chapter distinguishes bots from other automated systems, such as recommendation systems and data mining software.