Catalysts Of Content Distribution And Their Effect On Sentiment Analysis

The content on the social web is a rich source of information for people and organizations. This content can also contain a certain sentiment related to you, your brand or your products/services. In this post, I’ll be discussing the distribution of content—based on influence, virality and discussion—and the monitoring and analysis of positive or negative sentiment.

Access

When someone posts content on the Web, people will have access to it. This is a matter of fact and relates to all content and isn’t necessarily restricted to social media content, e.g. blog posts, status updates or user-created videos. It becomes social once distribution of content takes place outside the original creator’s direct control.

Distribution

The distribution of content can have an impact on you, your brand or your products/services if there is a clear sentiment involved. Sentiment is generally classified as positive, negative or neutral. In this post, I’m omitting “neutral”, because it doesn’t carry an opinion and in some cases, can even be positive because it enhances awareness.

I’d like to reflect on sentiment through three catalysts of distribution:

• Influence
• Virality
• Discussion

Influence

Influence is attributed to the person publishing and/or distributing a particular piece of content. That person’s influence is based on his/her authority (in knowledge, trust or experience) within his/her network.

Through influence, this network of friends/peers/fans is more likely to distribute the content that the influencer has published or distributed.

However, consider the following scenario:

Bill (INFLUENCER): “Don’t buy from <COMPANY X>. Their helpdesk kept me waiting for 45 minutes and then disconnected me.”

John (MEMBER OF BILL’S NETWORK): “I’ve heard from Bill that <COMPANY X> offers bad service.”

Paul (MEMBER OF JOHN’S NETWORK): “Have you seen this post from Bill that says <COMPANY X> sucks? Thanks for the heads, John!”

While this is a very simple example of content distribution, the essence remains this: three people are distributing a message that describes <COMPANY X> negatively. However, these aren’t three negative messages, but they’re three instances of the same message. The point is that distribution of content based on influence does not necessarily equal distribution based on agreement.

Virality

Content can spread like wildfire and earn a viral status, because they’re extremely funny, sad, absurd or well-made. An example is the “United Breaks Guitars” video from earlier this year:

People will spread content like this, but—as with influence—it doesn’t necessarily mean they share the sentiment portrayed in the content. In the “United Breaks Guitars” example, people may choose to spread the content because they’re amused by it rather than because they share a similar, negative sentiment about United Airlines.

Discussion

Agreement or disagreement over content definitely comes into play when we talk about distribution through discussion. Here, members of the network respond to the content by expressing their own stance. Let’s illustrate this by revisiting the two earlier examples mentioned under influence and virality:

Example 1:

Bill (INFLUENCER): “Don’t buy from <COMPANY X>. Their helpdesk kept me waiting for 45 minutes and then disconnected me.”

John (MEMBER OF BILL’S NETWORK): “I agree with Bill. <COMPANY X> once promised to send me a replacement product once and never did! They truly suck!!!”

Paul (MEMBER OF JOHN’S NETWORK): “That’s odd, I’ve never been treated badly by them. In fact, I’ve been a loyal customer of <COMPANY X> for 10 years!”

Example 2:

Dave: “Check out this video about United Airlines breaking guitars!”

Sam (MEMBER OF DAVE’S NETWORK): “That’s outrageous, but overblown in my opinion. United is one of the better airlines out there.”

Joe (OTHER MEMBER OF DAVE’S NETWORK): “Yea, I don’t like United. They lost two of my suitcases already!”

It becomes clear that in both cases, we’re not necessarily restricted to the distribution of a single message, but that multiple messages are involved. As a result, the sentiment of each message is unique as well and not a duplicate of the source.

This is the model I designed for content distribution and sentiment:

The model starts with a piece of content. The content is then accessed by people on the Web. It doesn’t matter whether this is free, paid, membership or freemium content. If it’s published, there will be people who have access to the content. After consuming the content, the viewer can choose to share/distribute the content. The distribution of the content can have one of three forms discussed in this post: influence, virality or discussion.

In case the content is distributed through influence and virality, it’s very likely that we’re dealing with the distribution of a single message (or sentiment).

When content is distributed through discussion, we’re more likely to see multiple messages (or sentiments) as people chime in with their opinions on the content or the parties involved in the content. Also, when content initially distributed through influence and virality spawn discussions, the original distribution of a single message becomes a distribution of multiple messages. However, the inverse is also possible. When content is initially distributed through discussion but someone publishes something that’s so strong that it branches into distribution through either influence or virality.

Sentiment Analysis

When you’re out to gauge sentiment about you, your brand or your products/services, it matters to know whether you’re dealing with a single or with multiple messages.

While it’s obviously not desirable to have negative content about you distributed through influence or virality, it helps to understand whether it’s the distribution of a single message seeded by the most influential members of people’s social networks. If that is the case, you might not have to deal with thousands or millions of negative members of the public but perhaps only a handful.

On the other hand, if thousands or millions of members of the social web are actively active in a discussion about you, it’s important to understand whether the messages (or sentiments) these individuals publish are unique and not a replication of someone else’s content/message/sentiment.

Making this distinction is still very difficult for machines even though we’ve come a long way in artificial intelligence (A.I.). It’s one of my focal points for 2010 to figure out how A.I. can contribute to sentiment analysis and other social business measurement/analytics topics.

There are solutions on the market that claim to be able to automate monitoring and analytics of brand mentions on the social web. However, to my knowledge, these tools are unable to distinguish between the distribution of a single sentiment and the distribution of multiple sentiments. In the case of the first example, these tools would count three negative brand mentions, while in reality, it is only one.

As a result, it’s most appropriate if sentiment analysis is performed by humans rather than machines. Humans can spot the difference between content distributed through influence, virality and/or discussion. Using sentiment analysis in this way will help you create a clearer picture of what is being said about you on the social web. It will also help you identify key influencers in your community’s social network. Sentiment analysis is not about keeping a tally of how many positive, neutral or negative sentiments you receive on the social web. A sentiment from a key influencer is more impactful than the sentiment of a lesser know person. Note that a key influencer’s sentiment is not necessarily more important but because s/he has more reach, the word-of-mouth effect is larger and thus more impactful.

Once you have an idea of what the content distribution looks like, how sentiments about you are built up and who key influencers or seeders are, you need to take action. This can vary from rather passive activities, like closer monitoring, to very active tasks, such as participating in the discussion or issuing a public response.

Go and find out what’s being said about you, your brand or your products/services. Identify whether these sentiments are distributed through influence, virality or discussion.

In the case of influence, find out:

• What the sentiment represented in the content is
• Who the key influencers are
• How members of the influencers’ network are distributing the message
• Whether you are dealing with a single sentiment or with multiple sentiments
• Whether discussion is being created out of the original content
• Whether it’s appropriate and how you can be involved in the content’s distribution

In the case of virality, find out:

• What the sentiment represented in the content is
• What is causing the content to go viral
• Who the key seeders are

• Whether you are dealing with a single sentiment or with multiple sentiments
• Whether discussion is being created out of the original content
• Whether it’s appropriate and how you can be involved in the content’s distribution

In the case of discussion, find out:

• What the sentiment represented in the content is
• How many unique sentiments are represented in the discussions taking place
• Whether key influencers are involved in the discussion
• Whether it’s appropriate and how you can be involved in the content’s distribution

If you have comments, experience or other nuggets of wisdom, let them be heard in the comments!

Photo credit: David Sutherland