A. Qualitative: Interviews with Expert Representatives of the Community
Despite the diversity of the five interview subjects in profession, age, denomination of Judaism practiced, and perspective on Israel, there were several consistent themes that came up. First, there is a consistent pattern of harassment against the Jewish American community by extremists: white supremacists, neo-Nazis, and aspects of the fragmented group generally known as the alt-right. Some of this harassment is bleeding into, and being taken up by, more mainstream U.S. conservatism. There was little evidence from interviews pointing to left-wing anti-Semitism. Second, much of the trolling and disinformation about Jewish Americans relies upon archaic stereotypes and conspiracies. These outmoded, and often hateful, clichés are commonly used to stoke fear and mistrust between other U.S. minority groups and American Jewish people. Third, there are platform specific differences in how harassment happens and what it looks like. This is due not only to trolls’ apparent obsession with anonymity, but also due to the demographics and affordances of particular platforms.
The interview subjects stated that, while they were familiar with the use of bots in spreading online propaganda, they were more concerned—and had more frequently experienced—human-based attacks on social media. Three of the five interview subjects, those most in the public eye, have been doxxed by prominent white supremacist and neo-Nazi leaders. All three attacks occurred following the publication of a news article. The two interviewees who work as politicians, one previously a candidate and the other about to take office, had their personal information and addresses leaked by known white nationalists following positive profiles on them and their work in the Jewish Telegraphic Agency (JTA). They told us that anti-Semitic individuals and groups seemed to be following reports from JTA and using them to choose Jewish American leaders to attack online. Angie, the soon to be elected 2018 political candidate, was doxxed by white supremacist leader David Duke. “I was getting random hate messages and tweets and didn’t realize it was because [Duke] had re-tweeted [the JTA profile],” she said. She illuminated the tactics of barraging her with hate messages: “I still don’t have that many followers, so I think they figured it would be overwhelming for me and it was.”
Ellen, a former political candidate, was similarly outed by Andrew Anglin. The Stormfront creator called her a “disgusting hissing weasel” and told his supporters to attack her. The next morning, her Gmail, Facebook, Instagram, and voicemail were flooded with thousands of messages. They used horrible slurs and contained threats of physical and sexual violence. There were dozens of such messages in her email. Because her phone number had been released she received a cascade of similarly harassing voicemails. The FBI got involved and police had to patrol the area surrounding her house during the election cycle. Nearly two years later, she continues to receive many threatening messages from trolls online. Although the number of messages has decreased, the graphic nature of the content has remained intense.
The other interviewees highlighted the fact that they get attacked online after they say or write anything public—this is especially true if the writing contains discussion of Judaism or anti-Semitism. Mark, a prominent economist and writer, said that his attackers “are not automated, they are [a] non-bot twitter mob.” He made it clear that although many of the accounts used messages and tactics similar to those of computational propagandists, further inspection revealed them to be real people. They weren’t tweeting at computationally enhanced levels, as bots often do, but they seemed to be organized as groups rather than as individuals. Jeff, a journalist, was doxxed on 4chan after he interviewed an attendee of the Unite the Right 2 Rally in 2018 and wrote the first name and home city of his interview subject. He was subsequently attacked via his digital wedding guestbook, where users wrote threats and used references to Hitler and the Holocaust (e.g. offering to buy him an oven for his cremation).
While each interview subject spoke of not wanting to let threats of the trolls impact their online activity, political campaigns, academic research or news reporting, they all admitted the threats of violence and deluges of anti-Semitism had become part of their internal equations. For some, it drove them to speak out louder and more vigorously, defying the trolls; for others, often citing concern over the harassment of family members, friends and romantic partners, sought to make adjustments. While we only spoke to a sample of five individuals, it was clear that, although anti-Semitic harassment has become almost normalized and expected following the election of Donald Trump, it has a chilling effect on Jewish Americans’ involvement in the public sphere.
B. Quantitative
Analysis of hashtags: First, it is evident that conservative hashtags were the main conduits of political conversation in our sample of 7,512,594 tweets (8,183,545 hashtags). Although we collected data from fewer conservative hashtags—94, as opposed to 106 liberal hashtags (Table 4)— conservative hashtags were tied to more tweets than all of the other categories combined, including six times the number of tweets linked to liberal hashtags (Table 1). Thus, it comes as no surprise that of the ten most popular hashtags in our sample, 80.26 percent of the related tweets pertained to conservative causes: #MAGA (2,300,281; conservative); #QAnon (989,277; conservative); #WWG1WGA (745,311; conservative); #Trump (739,803; conservative); #WalkAway (724,495; conservative); #Resist (540,973; liberal); #KAG (524,752; conservative); #FBRParty (341,096; liberal); #VoteThemOut (311,341; liberal); and, #TheResistance (288,067; liberal). Although, it is worth noting that while #Trump and #WalkAway (as in walk away from the liberal party) are categorized as conservative hashtags, a minority of liberals and other non-conservatives use them.
Significantly, the top ten hashtags reflect divide in discourse between supporters and opponents of President Trump. While #MAGA (Make America Great Again), Trump’s 2016 presidential campaign slogan, and #KAG (Keep America Great), Trump’s 2020 motto, were extremely prolific, producing 37.6% of the total tweets in the sample, their predominance is conventional. The abundance of #QAnon and #WWG1WGA is far more surprising. QAnon, is a conspiracy theory that emerged on the 4chan message board /pol/ on October 28, 2017. An anonymous figure, “Q,” named in reference to Q-level security clearance (Department of Energy authorization to access “Top Secret” information) posted a cryptic message reference Hilary Clinton and National Guard, and then a series of questions about Trump (Coaston, 2018). QAnon is based on the idea that Trump is in control of everything, and he is bringing “the storm” to disrupt the “deep state” history of U.S. presidential involvement in a global criminal empire hellbent on pedophilia (Coaston, 2018). There are strong anti-Semitic undertones, as followers decry George Soros and the Rothschild family as puppeteers. While there are several oft-repeated refrains, including “trust the plan,” “the great awakening,” “follow the white rabbit,” and “walk away,” the central saying is “where we go one, we go all” (WWG1WGA).
There is coordination surrounding the most popular liberal hashtags as well. #FBRParty stands for “follow back resistance,” and the hashtag is a somewhat codified way for liberals, progressives, and other members of “the resistance,” to both find other members and promote messages on a large scale. Users who share the hashtag are added to mass lists, and all members follow each other and tweet to gain more followers, creating a chain of message promotion and coordination. Members of this movement often have an emoji of a crashing blue wave in their username or the body of their tweet to signify their affiliation with the democratic party. Due to the speed at which they add each followers with these “follow back tweets,” individuals’ abilities to follow are occasionally slowed by Twitter or their accounts are taken down. Although not covered in this analysis, this chain of communication appears likely to have been targeted by malicious bot attacks.
Breaking down the sample by category of hashtag, tweets containing conservative hashtags consisted of 58.09 percent of the entire conversation, liberal hashtags tweets made up 31.01 percent, tweets with neutral hashtags were 9.51 percent, extremist hashtags comprised 0.88 percent. These percentages are notable for the sake of understanding our sample, but due to an arguably biased sampling of hashtags, they are not a perfect representation of American political conversation on Twitter. For instance, tweets were gathered from only 20 extremist hashtags, several of which were overly niche (#ProudOfYourBoy had 4 tweets and #Hammerskins had 1 tweet).
Analysis of terms within hashtags: Observing term occurrence by hashtag category, extremist tweets were more likely than any other category to contain derogatory and lean derogatory terms, as well as more likely to contain terms that can be derogatory depending on the context (Table 1). Given that words such as “shoah,” “nazi,” and “oy vey” are categorized as context-dependent, it is unlikely that these words are being used in non-derogatory ways on extremist channels of communication. In fact, these traditionally Jewish words, have been co-opted by extremists to mock Jewish people. For example, anti-Semites often write tweets saying things such as, “Oy vey the goyim know. Shut it down!” They also often write in poor imitation of Yiddish accent, ridiculing the Holocaust by claiming small inconveniences to be “annuda shoah” (or another shoah). Notably, around 15 percent of extremist tweets contained any term relating to anti-Semitism or Judaism. Given that the many extremists, especially white nationalists, target a number of minority groups and dedicate energy to condemning Antifa, liberals, and safe spaces, this is an interesting metric.
TABLE 1: Term Prevalence by Hashtag Category
|
|
Hashtag
Category
|
Total
Tweets
|
Derogatory + Lean Derogatory
(%)
|
Context Dependent
+ Lean Dependent
(%)
|
Neutral
(%)
|
Other
(%)
|
Contain Any Term
(%)
|
Conservative
|
4802207
|
0.82
|
1.65
|
0.32
|
0.01
|
2.79
|
Liberal
|
786225
|
0.53
|
1.26
|
0.16
|
0.006
|
1.95
|
Neutral
|
2563308
|
2.02
|
0.89
|
0.13
|
0.01
|
3.05
|
Extremist
|
72406
|
2.51
|
11.86
|
0.66
|
0.03
|
15.06
|
Certain hashtags appear to be correlated with the prevalence of specific terms. On a more general level, the ten hashtags that contained the highest percentages of tweets with derogatory or lean derogatory words were: #ReligiousRight (22.81%; conservative); #SCOTUS (5.63%; neutral); #NWO (5.60%; extremist); #WhiteGenocide (4.21%; extremist); #Libertarian (3.63%; conservative); #LiarInChief (3.29%; liberal); #FollowTheWhiteRabbit (2.55%; conservative); #Dems (2.3%; liberal); and, #TrumpTrain (2.21%; conservative). Both #ReligiousRight and #Uhruh had very few tweets (114 and 275 tweets respectively), so the large percentage of derogatory or lean derogatory words is more likely due to chance. Oddly the only term that was used in #RelgiousRight tweets was “NWO” (New World Order), which relates to a conspiracy that elites, often Jewish elites, are going to submit the entire world to servitude under totalitarian governance. It is tied to The Protocols of the Elders of Zion, an anti-Semitic treatise that was published in Russia in early 1900s. “NWO” was also by far the most common term found in #SCOTUS tweets, while “illuminati” and “globalist” (which is often an anti-Semitic slur) were the most popular terms in #NWO. For #WhiteGenocide, there was a spread of terms, but the most common was unsurprisingly “non-white.” Interestingly, far and away the most popular term used in #LiarInChief tweets was “soap.” Because extremists use soap in reference to the Holocaust, it is classified as lean derogatory, but upon further inspection of the term usage in #LiarInChief tweets, it is often used in reference to soap operas, likening Donald Trump’s presidency to a scripted televised drama. As for #Dems, the most common words were “Aryan” and “Nazi.” Not surprisingly, most accounts used these terms to accuse Trump and other republicans of being Nazis or obsessed with Aryan ideals, not espousing those values themselves. Lastly, the most popular term, by one to two orders of magnitude, used in #TrumpTrain tweets was “Soros,” in reference to George Soros, and the second most popular term was “NWO." Many of the tweets are in reference to QAnon or George Soros as a “puppeteer” paying for Antifa, Hilary Clinton, or any other person challenging far-right conservatism or extremists
Looking at the relationship between specific terms and hashtags (Table 6), the three terms that were most prevalent within specific hashtags were: “Nazi” (71.80% of #ProudBoys tweets, extremist hashtag); “Jew” (56.92% of #JCOT tweets, conservative hashtag); and “Hitler” (44.14% of #GoodbyeDemocrats tweets, conservative hashtag).
Automated “bot” accounts: To assess whether or not automated accounts impacted the U.S. political conversation surrounding Jewish Americans, particularly regarding the use of demeaning language, we analyzed accounts that tweeted five or more tweets during the collection time period that contained derogatory or lean derogatory terms. In all, 3733 accounts satisfied the requirements, but Botometer only returned the results for 3,060 accounts. It is unknown what happened to the 727 missing accounts. Although only speculative, it is possible they were removed by Twitter.
Botometer provides a number of scores, including the “universal score,” which is a language-independent CAP – essentially, the probability that the account is automated. We classified accounts with a universal score of greater than .5 as highly automated or botlike, and any accounts with lower scores as likely human. Previous work measuring computational propaganda using BotoMeter suggests that the program is more likely to rank bot accounts as false negatives than false positives. Accounts with a score of 50% or more bot-like demonstrate high degrees of automated behavior, though it is important to note that the distinction between bot (or automated account) and human account exists on a scale rather than as a binary (Woolley and Guilbeault, 2017). Of accounts that tweeted five or more derogatory or lean derogatory terms during the collection time period, 28.14 percent of the accounts are likely to be automated and 71.86 percent of the accounts are likely to be human (Table 2). Automated accounts produced 43.14 percent of tweets in this category and human accounts produced 56.86 percent. Preliminary examination suggests that between 30 and 40 percent of the accounts using derogatory terms were bots.
TABLE 2: Automation of Accounts
|
|
Category
|
Number of Accounts
|
Percentage of Total (%)
|
Number of Tweets
|
Likely Bot
|
861
|
28.14
|
426023
|
Likely Human
|
2199
|
71.86
|
561433
|
Interestingly, humans are more likely to tweet all categories of terms relating to Jewish Americans, including derogatory and lean derogatory terms (Table 3). This finding is in alignment with the qualitative interviews in Part A. Each of the interview subjects said that their most significant interactions with harassment were either due to doxxing or were from accounts that did not act or look like automated accounts. This is deeply concerning. If this problem was driven largely by automated accounts, there would be avenues for recourse, such as improved detection and dismantlement of bots, that would decrease the level of harassment. Given that this is largely a human problem, the path to a safe internet and public space for all is far more complicated, calling into question laws regarding hate speech and constitutional rights.
TABLE 3: Comparing Term Usage by Automated and Human Accounts
|
Category
|
Total
Tweets
|
Derogatory + Lean Derogatory
(%)
|
Context Dependent
+ Lean Dependent
(%)
|
Neutral
(%)
|
Other
(%)
|
Contain Any Term
(%)
|
Bot
|
426023
|
2.52
|
2.82
|
0.34
|
0.00
|
5.69
|
Human
|
561433
|
4.05
|
2.41
|
4.05
|
0.00
|
6.81
|