How to Kick-Start Your Viral Marketing Campaign

In my last post, I reviewed the ability of some of the most well known Twitter users to extend their reach through viral marketing. One well-known approach to viral marketing is to focus your message on a small number of highly influential people, who will then help to start a word-of-mouth chain reaction that effectively broadcasts your message to a wide audience at a low cost.  Using this strategy requires that you can identify the most highly influential individuals in your target market. New research is now available to help facilitate the indentification process. Four researchers at the Max Planck Institute for Software Services recently published a landmark paper investigating how to measure and identify influence in social networks.

Measures of Influence

The researchers focused on Twitter users. With the cooperation of Twitter, they compiled a dataset used for the research that comprised more than 1.7 billion tweets among 54 million Twitter users containing nearly 2 billion follow links.

The researchers compared three different measures of user influence on Twitter:

• Indegree Influence, or the number of followers that a user has, an indicator of that user’s popularity.
• Retweet Influence, the number of retweets in the dataset containing a users’s name, a measure of their ability to propagate a message among their followers.
• Mention Influence, or the number if tweets containing a user’s name, indicating the ability of the user to initiate and maintain conversations with others.

The Million Follower Fallacy

One of the most interesting questions tackled by the study was to what degree the three measures of influence were correlated.  The researchers focused on the 6 million most active Twitter users, and ranked each one according to each of the three measures. They then examined the correlation between the rankings, shown in the following table:

Correlation ranges on a scale of -1 to 1; a perfect positive correlation is 1 (meaning that a high rank in one measure tends to occur along with a high rank in another measure); a perfect negative correlation is -1 (meaning that a high rank in one measure tends to occur with a low rank in another measure); no correlation is indicated by a score close to 0. All three measures of influence were positively correlated. However, ties in rank among the lowest ranked in the 6 million active Twitter users artificially generated the relatively high correlation seen in the column “All” in the above table. The researchers therefore  isolated the top 10% and top 1% of users based on their number of followers, and examined the correlations between the three measures of influence. The researchers reached the following conclusion:

After this filtering step, the top users showed a strong correlation in their retweet influence and mention influence…This means that, in general, users who get mentioned often get rewteeted often, and vice versa. Indegree, however, was not related to the other measures. We conclude that the most connected users are not necessarily the most influential when it comes to engaging one’s audience in conversations and having one’s messages spread.

This phenomenon has been dubbed “the million follower fallacy” and is one of the most important conclusions of the study: if your goal is to identify the users who are most likely to repeat your message to others in a viral marketing campaign, don’t look for the users in your target market with the most followers, look for the users with the most retweets and mentions.

As you move event further up the rankings, the overlap between the top 100 ranked users according to the three different measures of influence becomes smaller:

There were 233 distinct users that made the top 100 ranking in one or more of the three measures, and 67 that appeared on more than one of the top 100 lists.

Influence Across Different Topics

Another key question the team investigated was whether a user’s influence varied by different genres of topics. To address this question, they examined top-ranked influencers for three different topics which were among the most mentioned during 2009: the Iranian presidential elections, the H1N1 flu virus, and the death of Michael Jackson. Among the set of users who tweeted about any of these topics, only 2%, a set of 13,219 users, tweeted about all three topics, demonstrating the diversity of the topic genres.

Once again, the team looked at the correlations between the rankings on the topics, this time looking specifically at retweets and mentions among the most popular users, as measured by Indegree Influence.

Given the relatively high correlations among the most popular users in their ranks for retweets and mentions across these diverse topics, the researchers concluded that opinion leaders can hold sway over a wide variety of topics. This means that the opinion leaders can help spread a message outside their area of expertise. This is consistent with recent efforts to insert advertising links into popular user’s tweets. The fact that the degree of influence of users is a long-tail (power law) distribution also leads the authors to conclude that it is more economical to target top influentials to kick start a viral marketing campaign, rather than a massive number of less influential users.

How to Become Influential

The team next looked at three groups of users who tweeted about only one of the topics, to determine what factors make ordinary users more influential. These three groups of users had from 3 to 180 times fewer followers than the highest ranked influencers. In comparison to the top ranked influencers across all topics, this set of users that tweeted about only one of the topics saw their influence rise to a much greater degree over an eight month time period in 2009.

This lead the authors to conclude that through concerted effort, and focus on a single topic, users had the greatest chance of increasing their influence over time.

Summary

The key findings of the research are as follows:

• It is easier to kick-start a viral campaign by focusing on top influencers, rather than large numbers of individuals with a small degree of influence. This follows from the fact that all three measures of influence fall into a long-tail (power) distribution.
• If you are targeting individuals with a message with a view to extending your reach through viral marketing, the number of followers an individual has is less important than the number of times they retweet, and in turn, are retweeted. The number of followers a Twitter user has may indicate popularity, but this measure has a weak correlation to retweets and mentions.
• The most influential users can hold influence over a variety of topics, as measured by retweets and mentions.
• Ordinary Twitter users can best gain influence by focusing on a single topic, and consistently including links to useful and engaging content in their tweets, as opposed to focusing on conversations with other users.

Unanswered Questions

Like all good research, the analysis presented by the authors suggests further areas for investigation:

• Are the same influence patterns evident in business-oriented communications, as opposed to the general interest topics specifically investigated in this research?
• The research focused on the users with the most rewteets and mentions. The data was not normalized to account for the number of their followers. What patterns emerge when the data is adjusted in this manner?
• Are retweets driven by the influence of the user, the content of the tweet, or the content of the link? If it is driven by all three factors, what is the relative importance of each?
• How can the research be used to predict the outcomes of social media campaigns?

The researchers are currently working with Twitter to make their entire dataset available to researchers. While the dataset is not yet available, you can check their website for updates on their data sharing plan. If and when the dataset becomes available, we can look forward to further detailed investigation of user influence in social media.