Social Media Diffusion


Good Enough?

Does it make a difference what and how you post on social media? Don’t you just need to maintain a presence? Or if you do not have a presence how do you know the right message is getting out there. It is all about emotion, quantity and speed. It makes a difference, because social media is exploding.

Social Media Explosion

How fast are social networks growing?

  • Percentage of users ages 16-64 increasing social media use – 43%
  • Annual growth in total number of social media users – 10.5 % =376 million

Across age groups, where are they saying they are using social networks:

  • Discover brands/products via ads on social for discovery – 27%
  • Discover brands/products via recommendations on social – 24%
  • Research products online via social – 43%

Simultaneous to audience growth, information being added to the internet will grow from 4.4 billion GB per day in 2016 to 463 billion GB per day in 2025 (IDC estimate). With exploding users and content projected to grow a 100-fold per day, how do you differentiate your messaging to gain, maintain and grow followers, engagement and sales? How do you make sure your content influences and accelerates through the internet, so you get your, “share of eyeballs?”

Social Media Diffusion

Information diffusion is how fast data is moving through a network. It has been studied extensively in social, physical and computational sciences. Research in word of mouth and viral marketing has been documented in business literature. With the emergence of social media, new communication techniques have been explored such as: SMS, weblogs, picture-sharing portals and online communities. The following research considers the variables that effect the diffusion on information on social networks.

Sentiment and Social Media Quantity and Speed

Steiglitz and Xuan conducted research on the effect of emotion on political tweets. This research analyzed 64,432 tweets posted one week before two German state parliament elections. They proved the following hypotheses:

  • The larger the total amount of sentiment (positive or negative) a political Twitter message exhibits, the more often it is retweeted.
  • The larger the total amount of sentiment (positive or negative) a political Twitter message exhibits, the shorter is the time lag to the first retweet.

Using supervised learning (regression) the study considered: 

Dependent variables

  • Number of times the tweet has been retweeted
  •  Time lag between the tweet and the first retweet

Independent variables

  • Total amount of sentiment
  • Number of hashtags
  • URL inclusion
  • # of user’s followers
  • Number of tweets posted during the sample period activity.

The regression models’ coefficients indicated that for every unit increase in negative words there was a 6% increase in tweets(pg. 238). Likewise, for every unit increase in positive words there was a 4% increase in tweets. An important hypothesis they were not able to prove:

  • The association between sentiment and retweet time lag is stronger for tweets with negative sentiment than those with positive sentiment.

Sentiment and Social Media Predictability

Ashan and Kumari researched 20,000 tweets on the 2016 U.S. presidential election. The analysis considered the impact of sentiment along with the following environmental factors in predicting information diffusion:

  • URL’s
  • Hashtags
  • Number of followers
  • Account age
  • Tweet age
  • # User created tweets

Using two different regression approaches, analysis was completed determining the independent variables that provided the best model predictability. As seen below, in each model sentiment content was included and provided a significant increase in predictability.

Graphical user interface, chart

Description automatically generated with medium confidence

Including the sentiment content significantly improves the predictability of social media performance and is enhanced with the ability to consider hashtags.

Sentiment and Positivity

Ferrara and Yang conducted a study of 19 million tweets with the following distribution:

Distribution of polarity scores computed for our dataset.

Their findings indicated that positive tweets reach a large audience and are shared more often. As tweet score becomes more positive, the number of retweets, favorites and seconds to first retweet increases at an accelerated rate.

Just how much of a difference does positivity make:

  • Positive tweets are favorited 5 times the rate of negative tweets.
  • Positive tweets are retweeted 2.5 time faster than neutral or negative tweets.

So What? – Words Make a Difference

These are the key findings on sentiment content:

  • Increases the quantity of social media traffic and its speed through social media channels
  • Used in addition to environmental factors improves the predictability of social media diffusion
  • Increased positive content increases social media acceptance and diffusion

At Yalo, we feel that the words really make a difference. There are many environmental factors that also need to be considered. With these factors we have varied levels of control. When is comes to how we share content we have complete control. Control of what we share and also how we share it. Sentiment analysis is the tool to tune content delivery for influencing followers and customer, as well as, analysis of social media traffic reflecting brand image.