Tracking Feelings: The Emotions Behind Like

In the chapter “Focus on Feelings” from Jonah Berger’s book Contagious: Why Things Catch On, the author emphasizes the importance of emotions in making products, ideas, and messages go viral. Berger explains that emotions drive people to share content, and high-arousal emotions like anger and anxiety are particularly effective.

The key takeaway is that to make something contagious, you need to tap into the underlying emotions that motivate people to take action. This means crafting messages that evoke strong feelings, whether positive or negative, to kindle the fire and drive people to share.

To illustrate the power of emotions in making content go viral, Jonah Berger shares the story of Anthony Cafaro, a designer at Google. Cafaro was part of a team working on Google’s Art Project, which aimed to bring the world’s art museums online. Despite the project’s potential, it struggled to gain traction initially.

Cafaro decided to create a heartfelt video that focused on the emotional impact of the project. The video showcased how the Art Project could bring joy and inspiration to people by allowing them to experience art from anywhere in the world. This emotional appeal resonated with viewers, leading to a significant increase in shares and engagement.

The key lesson from Cafaro’s story is that tapping into emotions, especially those that evoke a strong response, can dramatically enhance the virality of content. By focusing on how the Art Project could make people feel, Cafaro was able to turn a struggling initiative into a widely celebrated success. However, Berger has shown in his study that videos go viral because they capture people’s emotions. Viewers engage with social media posts when they recognize a feeling while watching the video. It appears that posts with high likes, comments, and shares are those that emotionally capture the viewers’ attention. Therefore, you can measure viewers’ feelings by tracking the number of likes, comments, and shares.

But how can we measure the kinds of feelings triggered by a post on social media? Do people feel awe, joy, guilt, hostility, worry, or something else? What kinds of feelings are behind the thousands of likes? There isn’t an easy way to address this question. However, most companies around the world are using artificial intelligence (AI) to try reply to these questions.

Sentiment analysis always comes up when analytics teams measure feelings on social media. Rather than simply counting mentions or comments, sentiment analysis considers feelings and opinions. Using Natural Language Processing (NLP) and Machine Learning (ML), it is possible to categorize emotions based on large datasets of comments on social media posts and determine whether the sentiment is positive (love, amazing, great, best, perfect), negative (bad, awful, terrible, worst, hate), or neutral (okay, so-so, not bad, alright).

Some tools can help you in this process, such as: Hootsuite Listening powered by Talkwalker, Talkwalker by Hootsuite, Brand24, Meltwater, and Idiomatic.

Another way to measure feelings on social media is through user surveys. Collecting data from users about their emotional responses to social media content can provide insights into the emotional impact of browsing social media. These kinds of surveys are prevalent on the YouTube platform, such as the well-known YouTube Advertiser Survey, which invites viewers to answer a short question.

Social media sentiment analysis is also a crucial part of any good social listening strategy. Sometimes called “opinion mining,” it involves digging into the words and context of social posts to understand the opinions they reveal. Measuring social sentiment is an important aspect of any social media monitoring plan and can help you predict your next viral post. But this is a theme for the next article.

About Ligia Galvao

Master in Consumer Intelligence and Analytics from Pace University. 4x Tableau Social Ambassador, LinkedIn Learning instructor, and leader of the Data + Women LATAM and Brazil Tableau User Group communities.

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