If the immediate goal of the action of trolling is to cause disruption or provoke emotional reactions, what is it that makes people want to do this disruption or provoking of emotional reactions? Some reasons people engage in trolling behavior include: Amusement: Trolls often find the posts amusing, whether due to the disruption or emotional reaction. If the motivation is amusement at causing others’ pain, that is called doing it for the lulz [g6]. Gatekeeping: Some trolling is done in a community to separate out an ingroup from outgroup (sometimes called newbies or normies). The ingroup knows that a post is just trolling, but the outgroup is not aware and will engage earnestly. This is sometimes known as trolling the newbies. Feeling Smart: Going with the gatekeeping role above, trolling can make a troll or observer feel smarter than others, since they are able to see that it is trolling while others don’t realize it. Feeling Powerful: Trolling sometimes gives trolls a feeling of empowerment when they successfully cause disruption or cause pain.** Advance and argument / make a point: Trolling is sometimes done in order to advance an argument or make a point. For example, proving that supposedly reliable news sources are gullible by getting them to repeat an absurd gross story [g5]. Punish or stop: Some trolling is in service of some view of justice, where a person, group or organization is viewed as doing something “bad” or “deserving” of punishment, and trolling is a way of fighting back.

Trolling is when an Internet user posts inauthentically (often false, upsetting, or strange) with the goal of causing disruption or provoking an emotional reaction. When the goal is provoking an emotional reaction, it is often for a negative emotion, such as anger or emotional pain. When the goal is disruption, it might be attempting to derail a conversation (e.g., concern trolling [g4]), or make a space no longer useful for its original purpose (e.g., joke product reviews), or try to get people to take absurd fake stories seriously [g5].

In 2016, the Twitter account @Sciencing_Bi was created by an anonymous bisexual Native American Anthropology professor at Arizona State University (ASU). She talked about her experiences of discrimination and about being one of the women who was sexually harassed by a particular Harvard professor. She gained a large Twitter following among academics, including one of the authors of this book, Kyle. Separately, in 2018 during the MeToo movement [f7] , one of @Sciencing_Bi’s friends, Dr. BethAnn McLaughlin (a white woman), co-founded the MeTooSTEM non-profit organization, to gather stories of sexual harassment in STEM (Science, Technology, Engineering, Math). Kyle also followed her on Twitter until word later spread of Dr. McLaughlin’s toxic leadership and bullying in the MeTooSTEM organization (Kyle may have unfollowed @Sciencing_Bi at the same time for defending Dr. McLaughlin, but doesn’t remember clearly). Then, in April 2020, in the early days of the COVID pandemic [f8], @Sciencing_Bi complained of being forced to teach in person at ASU when it wasn’t safe, and then began writing about their COVID symptoms. Fig. 6.2 Tweet from @Sciencing_Bi complaining about covid, sexual harassment (Title IX) descrimination for their race (BIPOC), and mentioning that they are still posting anonymously. (screenshot source)# On July 31st, Dr. McLaughlin announced that @Sciencing_Bi had died of COVID. Fig. 6.3 Tweet from Dr. McLaughlin reporting that @Sciencing_Bi died of COVID. (screenshot source [f9])# Many academics on Twitter expressed sympathy and outrage over what had happened to @Sciencing_Bi. Dr. McLaughlin held a memorial service for @Sciencing_Bi online via Zoom. A few people showed up to Dr. McLaughlin’s online Zoom memorial service for @Sciencing_Bi, but the guests felt something was off. Afterward, Mr. Eisen began to search for any evidence that @Sciencing_Bi had been a real person. He could not find any. “The combination of the weird things that were happening on the call and looking at the tweets and seeing how much they circled BethAnn, it just became obvious to me,” he said. “‘Oh, [@Sciencing_Bi] is BethAnn.’” The Anonymous Professor Who Wasn’t - The New York Times [f10] Arizona State University confirmed that they had no professors who matched the description of @Sciencing_Bi. Dr. McLaughlin’s and @Sciencing_Bi’s accounts were suspended from Twitter for violating Twitter policies, and Dr. McLaughlin eventually confirmed that she had completely invented @Sciencing_Bi.

As a rule, humans do not like to be duped. We like to know which kinds of signals to trust, and which to distrust. Being lulled into trusting a signal only to then have it revealed that the signal was untrustworthy is a shock to the system, unnerving and upsetting. People get angry when they find they have been duped. These reactions are even more heightened when we find we have been duped simply for someone else’s amusement at having done so.

One of the early ways of social communication across the internet was with Email [e5], which originated in the 1960s and 1970s. These allowed people to send messages to each other, and look up if any new messages had been sent to them.

As we talked about previously in a section of Chapter 2 (What is Social Media?), pretty much anything can count as social media, and the things we will see in internet-based social media show up in many other places as well. The book Writing on the Wall: Social Media - The First 2,000 Years [e1] by Tom Standage outlines some of the history of social media before internet-based social media platforms such as in times before the printing press: Graffiti and other notes left on walls were used for sharing updates, spreading rumors, and tracking accounts Books and news write-ups had to be copied by hand, so that only the most desired books went “viral” and spread Later, sometime after the printing press, Stondage highlights how there was an unusual period in American history that roughly took up the 1900s where, in America, news sources were centralized in certain newspapers and then the big 3 TV networks. In this period of time, these sources were roughly in agreement and broadcast news out to the country, making a more unified, consistent news environment (though, of course, we can point out how they were biased in ways like being almost exclusively white men). Before this centralization of media in the 1900s, newspapers and pamphlets were full of rumors and conspiracy theories [e2]. And now as the internet and social media have taken off in the early 2000s, we are again in a world full of rumors and conspiracy theories.

Metadata is information about some data. So we often think about a dataset as consisting of the main pieces of data (whatever those are in a specific situation), and whatever other information we have about that data (metadata). For example: If we think of a tweet’s contents (text and photos) as the main data of a tweet, then additional information such as the user, time, and responses would be considered metadata. If we download information about a set of tweets (text, user, time, etc.) to analyze later, we might consider that set of information as the main data, and our metadata might be information about our download process, such as when we collected the tweet information, which search term we used to find it, etc.

In this screenshot of Twitter, we can see the following information: The account that posted it: User handle is @dog_rates User name is WeRateDogs® User profile picture is a circular photo of a white dog This user has a blue checkmark The date of the tweet: Feb 10, 2020 The text of the tweet: “This is Woods. He’s here to help with the dishes. Specifically, the pre-rinse, where he licks every item he can. 12/10” The photos in the tweet: Three photos of a puppy on a dishwasher The number of replies: 1,533 The number of retweets: 26.2K The number of likes: 197.8K

There are several ways computer programs are involved with social media. One of them is a “bot,” a computer program that acts through a social media account. There are other ways of programming with social media that we won’t consider a bot (and we will cover these at various points as well): The social media platform itself is run with computer programs, such as recommendation algorithms (chapter 12). Various groups want to gather data from social media, such as advertisers and scientists. This data is gathered and analyzed with computer programs, which we will not consider bots, but will cover later, such as in Chapter 8: Data Mining. Bots, on the other hand, will do actions through social media accounts and can appear to be like any other user. The bot might be the only thing posting to the account, or human users might sometimes use a bot to post for them. Note that sometimes people use “bots” to mean inauthentically run accounts, such as those run by actual humans, but are paid to post things like advertisements or political content. We will not consider those to be bots, since they aren’t run by a computer. Though we might consider these to be run by “human computers” who are following the instructions given to them, such as in a click farm:

Act with unforced actions in harmony with the natural cycles of the universe. Trying to force something to happen will likely backfire. Rejects Confucian focus on ceremonies/rituals. Prefers spontaneity and play. Like how water (soft and yielding), can, over time, cut through rock. Key figures: Lao Tzu ~500 BCE China Lao Tzu Zhuangzi ~300 BCE China

Being and becoming an exemplary person (e.g., benevolent; sincere; honoring and sacrificing to ancestors; respectful to parents, elders and authorities, taking care of children and the young; generous to family and others). These traits are often performed and achieved through ceremonies and rituals (including sacrificing to ancestors, music, and tea drinking), resulting in a harmonious society.