How did these Twitter accounts gain influence?
The IRIB, utilizing a strategy called astroturfing, promoted their viewpoints under the guises of fake organizations, usernames, contact information, and stock images. In this way, they were able to spread messages to their respective audiences via a faux grassroots campaign, growing the popularity of their posts and accounts steadily from 2012 to 2018:
IRIB influence in 2012
IRIB influence in 2018
When were these accounts the most active?
Gaining a following requires momentum. In order to gain influence, the IRIB likely leveraged current events in order to frame the discourse around topics that were relevant in the minds of Twitter users. The following interactive visualization shows possible evidence for this strategy at play:
Here we can see how the campaign grew over time, and how news events (annotated with circles) may have helped the campaign spur increases in Twitter interaction (reflected in spikes in reply, like, quote, and retweet counts). Although these associations may be confounded by lurking variables (e.g. higher news coverage increases overall Twitter activity, increasing interaction metrics), it does allow us to begin thinking like an information operative, and the possible opportunism involved in garnering attention on social media.
We invite the reader to explore the relationship between time and tweet interactions by:
- Zooming into specific time ranges by dragging the slider to specify a time window.
- Investigate significant events that align with spikes in interactions by hovering over the blue circles. For example, the largest spike in the chart aligns with the Trump's Meeting with Kim Jong Un and highlighting how information operatives may have levarged this meeting to sway public opinion.
- Inspecting the specific Twitter interaction metrics on a given day by hovering over the bars.
What were their popular accounts tweeting about?
From looking at the events in the last visualization, we may be able to infer broadly what events or subject matter were being discussed by these operatives, but what exactly were they saying? What were these accounts actually tweeting about that so many users found worth reading?
The following is a curated representation of how Twitter users experienced tweets that originated from the Top 10 accounts with the highest average followerships. For example, the user with the most followers, السعودية تايمز, had mainly tweeted about content related to the Islamic religion. In order to experience the kinds of tweets that a random user may have experienced during the campaigns most active periods (2015 to 2018), we invite the reader to:
- Examining a top IRIB account (ordered from most to least followers) by selecting from the radio button list to the left. Note that the numbers next to each account are the number of tweets the account generated from 2015 to 2018 in order to put the uncertainty of sampling into context.
- Examining a specific time range for tweets by selecting a start and end year.
- Clicking “Sample” in order to see a random sampling of the specified account’s tweets within the specified time range.
What were the recurring topics and themes espoused by these accounts?
Now that we have a sense for the type of content that the major players were tweeting, let’s take a step back and see what topics were popular items of discussion throughout this campaign, and how successful they were at garnering likes, retweets, etc.
The following gives a similar sense as the previous visualization about the experience that Twitter users may have had during this campaign, but augments the view to shed a light on the topics, hashtags, and tweets that gained the most traction (sorting tweets from most to least likes). For example, in the Israel category, one of the recurring topics was the Israel-Palestine conflict, particularly territorial claims for Jerusalem. We invite the reader to discover these recurring themes and how successful they were at garnering attention by filtering by a set of topics (manually categorized by subject matter similarity based on the Top 50 most prevalent hashtags), as well as the Top 5 most frequent hashtags within these categories (sorted from most to least frequent).
How was the campaign trying to shift the the conversation through time?
Finally, after examining the topics and hashtags that gained the most likes, let’s examine the hashtags that were the most prevalent during this campaign, and how this changed through time. Looking at frequency of hashtags across the years in isolation, we can begin to see the discussions IRIB was focusing on promoting at different points in time.
The years with the highest activity (2017-2018) are indicative of another important by the FireEye investigation: the operations were fully mobilized after the 2016 election. This gives credence to the effect of current events explored in the second visualization.
We invite the reader to explore how the main tweet topics changed over time by:
- Progressing the slider to different years during the time period when the hashtags that appeared at least 500 times within a given year occurred (2014 - 2018).
- Hovering over a circle for a given hashtag to focus-in, as well as investigate the precise frequencies of a hashtag for a given year.
Conclusion
The Iran influence campaign provides an example of how social media can provide access for foreign actors to influence the discourse of other nation states. From possibly taking advantage of current events, to focusing on specific topics at different points in time, the IRIB was able to tap into a network of fake, but influential accounts to spread their ideas.
We’ll never know exactly how much influence the Iran campaign had on political discourse. However, we do know that in a democracy, information operations of this scale have the potential to distort the true issues within a society, calling upon us to think twice about the information, and actors, on these social media platforms.
Note: In making this project we had to make some assumptions about the data (e.g. that likes corresponded to influence, despite the possibility of bot networks and within-campaign liking/retweeting/etc.), as well as making several biased decisions in order to threshold (e.g. choosing the 500 count cut-off for the final visualization) and give meaning to the data (e.g. manually created categories for the second to last visualization). Our findings are meant to be interpreted as primarily exploratory in nature, providing possible future directions of inquiry for more rigorous analyses.Our Team:
