Characterizing social media manipulation in the 2020 U.S. presidential election

Characterizing social media manipulation in the 2020 U.S. presidential electionKey InsightsIntroData CollectionDescriptive Stats on DatasetTop 30 hashtags and mentionsTwitter unhashed banned user datasetBot DetectionCharacterizing User Political BiasAutomationTop 15 Hashtags Utilized by BotsTop 15 Hashtags Utilized by HumansBots in Campaign DiscourseRepublican Campaign-related HashtagsDemocratic Campaign-related HashtagsHashtags Utilized by Right-leaning Humans/BotsHashtags Utilized by Left-leaning Humans/BotsTweets Split by Party and BotnessRepublican Bots Change Tweeting Behavior Around DNCHuman-bot Interactions and Echo ChambersForeign Interference OperationsPolitical Bias of Foreign Interference AccountsPolitical Affiliation and Banned User InteractionDistortionThe Conspiracy TheoriesNine Popular ConspiraciesTop four tracked conspiracy theory hashtagsSelf-Reported Geographic Location of Q-Anon UsersConspiracies and Media BiasPolitical Ideology and Conspiracy EndorsementConspiracies and BotsHyper-partisan Media Outlets and Bots

Key Insights



Using a combination of state-of-the-art machine learning technologies and human validation, we investigate a number of research questions pertaining to two signatures of manipulation:

  1. Automation —> evidence for adoption of automated accounts governed predominantly by software rather than human users
  2. Distortion —> of salient narratives of discussion of political events, e.g., with the injection of inaccurate information, conspiracies or rumors


Data Collection

Descriptive Stats on Dataset

Top 30 hashtags and mentions

Twitter unhashed banned user dataset

Bot Detection

… use a conservative approach to classify bots as accounts that sit at the top end of the bot score distribution, rather than carrying out a binary classification of accounts into bots and humans

This addresses the problem of determining the nature of borderline cases for which detection can be inaccurate, and conversely allows to focus on accounts that exhibit clear bot traits. The results will be manually validated for accuracy.

Characterizing User Political Bias

Similar to prior work (Bovet and Makse, 2019; Badawy, et al., 2019), we identify a set of 29 prominent media outlets that appear on Twitter.



Top 15 Hashtags Utilized by Bots

Top 15 Hashtags Utilized by Humans


Bots in Campaign Discourse

While we do recognize that there may be users who use these hashtags in tweets with opposing viewpoints, vast amounts of research in political polarization assert that that is relatively infrequent (Jiang, et al., 2020; Bail, et al., 2018). Hence, we selected hashtags that were most relevant to both campaigns, such as “trump2020” and “bidenharris2020”.

Republican Campaign-related Hashtags

Democratic Campaign-related Hashtags

Hashtags Utilized by Right-leaning Humans/Bots

Hashtags Utilized by Left-leaning Humans/Bots


Tweets Split by Party and Botness

Republican Bots Change Tweeting Behavior Around DNC


Human-bot Interactions and Echo Chambers

This indicates a slightly higher propensity of within-cohort interaction for right-leaning users. Left-leaning users retweet around 13 percent across the aisle, whereas right-leaning users retweet 10 percent across the aisle.

This indicates left-leaning bots have a more diverse retweet appetite than right-leaning bots do


Foreign Interference Operations

Looking at the Twitter data of foreign interference operations, where users are categorized by country, the authors determine the propensity of these accounts, by country, to be either left- or right- leaning politically. [Fig 3.]

Political Bias of Foreign Interference Accounts


Political Affiliation and Banned User Interaction

Next, they plot specific targets of banned users, and their relative position in the Twitter network.


  1. Roughly six left-leaning clusters (in blue) and two right-leaning clusters (in red).

    1. This indicates that right-leaning users are more tightly knit than left-leaning users are
  2. We also show the position of banned Chinese-ops users (green diamonds) and Russian operations (yellow diamonds)

Since banned users often interact with Twitter celebrities, the users shown are ones exclusive to each cohort. That is, yellow diamonds are users who have only been associated with banned Russian accounts.

  1. We also observe that Chinese state-sponsored users tend to interact with Republican users more.

  2. Russian sponsored interactions also emerged outside of the right-leaning and left-leaning cores.

    1. Together, these suggest information operations are targeted toward specific communities based on partisan or ideological leanings



In order to stay away from the "conundrum" of establishing veracity, the authors decide to focus on conspiracy theories.

Conspiracy theories are most likely false narratives, oftentimes postulated upon rumors or unverifiable information, that appear in social networks shared by users or groups with the aim to deliberately deceive unsuspecting individuals who genuinely believe in such claims (van Prooijen, 2019)

The Conspiracy Theories

They focus on three conspiracy theories:

  1. QAnon: A far-right conspiracy movement which has gained popularity in the run up to the 2020 election. This group’s theory suggests that President Trump has been battling against a satan—worshipping global child sex-trafficking ring and an anonymous source called ‘Q’ is cryptically providing secret information about the ring (Zuckerman, 2019).

    1. These users frequently use hashtags such as #qanon, #wwg1wga (where we go one, we go all), #taketheoath, #thegreatawakening and #qarmy.
  2. “gate” conspiracies: Another indicator of conspiratorial content is signalled by the suffix ‘-gate’ with theories such as obamagate, an unvalidated claim against the Barack Obama’s officials that allegedly conspired to entrap Trump’s former national security adviser, Michael Flynn, as part of a larger plot to bring down the then-incoming president. Another example of “gate” conspiracy theory is pizzagate, a debunked claim that connects several high-ranking Democratic Party officials and U.S. restaurants with an alleged human trafficking and child sex ring.

  3. Covid conspiracies: A plethora of false claims related to the coronavirus pandemic emerged recently. They are mostly about the scale of the pandemic and the origin, prevention, diagnosis, and treatment of the disease. The false claims typically go alongside the hashtags such as #plandemic, #scandemic or #fakevirus.

General Observations:

Nine Popular Conspiracies


Word Clouds of co-occurring hashtags were utilized to find additional hashtags that were popular and to further investigate.


Top four tracked conspiracy theory hashtags

We see that conspiracy theory hashtags tend to drop off in mid-July, this is likely due to Twitter engaging in a takedown of over 7,000 QAnon associated accounts.


Self-Reported Geographic Location of Q-Anon Users

57.6% of users report a location in their Twitter profile...


Conspiracies and Media Bias

They then look at whether left vs. right users share conspiratorial narratives differently

Political Ideology and Conspiracy Endorsement

Note: I believe the two shades of red/brown are supposed to be the same color (conspiratory). This distinction in color is not mentioned in the publication at all so this is my own assumption.

Almost a quarter of users who endorse predominantly right-leaning media platforms are likely to engage in sharing conspiratory narratives. Out of all users who endorse left-leaning media, approximately two percent are likely to share conspiratory narratives.


Conspiracies and Bots

The main question [the authors] seek to answer is: are bots used to target groups and how do they push conspiracy narratives with news related media?

This overlap suggests that users who share conspiracy related content are prone to adopting multiple conspiracy narratives and that the communities are highly connected.


Hyper-partisan Media Outlets and Bots

The authors also investigate the proportion of users sharing URLs from these news Web sites who have also used QAnon hashtags at any point in our dataset


Also explore the proportion of bots and compare it to their political leaning and usage of conspiratory language.

It is possible that such observations are in part the byproduct of the fact that bots are programmed to interact with more engaging content, and inflammatory topics such as conspiracy theories provide fertile ground for engagement (Stella, et al., 2018). On the other hand, bot activity can inflate certain narratives and make them popular. The automated accounts that are a part of an organized campaign can purposely propel some of the conspiracy narratives, further polarizing the political discourse.


Notes by Matthew R. DeVerna