Reputation Management with SM2, Sentiment notes and Reputation ROI
The super-hot political season we’re in is a great time talk about reputation management in social media as professional smear artists work overtime spreading rumors about candidates of all stripes. Obama’s campaign has aggressively recognized this with their Fight The Smears site where they immediately publish any new rumors and refute them decisively in real time. I don’t know if they are using social media monitoring to follow what people are saying but I think it’s likely. This kind of proactive reputation defense requires a combination of technology and human involvement.
For example, though we offer a sentiment indicator in our analysis tools it is just that: an indicator. It identifies words and phrases in context that it thinks could be indications of a negative or positive statement related to a keyword in that search. If it saw ‘Obama sucks‘ in a blog post it would likely flag that as negative. This is where the human beats the computer every time however. Using our drill down feature you can read the ‘negative’ statement in context. Suppose it actually says:
‘ Obama sucks down a frosty at a local fast food joint while talking to a smiling group of fans’
The computer thinks that’s negative, any human knows it’s positive and would correct the sentiment in SM2 accordingly. Yes, it’s labor intensive but not as intensive as rebuilding a reputation damaged by an untruth or misconception.
Sentiment and Accuracy Claims
Semantic search offers up the Holy Grail of search, search that understands natural language queries such as:
‘which dealer in Rochester has a blue Civic in stock?’
The amount of things a search engine would have to understand to return an accurate answer to this question is mind-boggling. It would have to know that Civic and dealer in the same sentence probably means a car is involved, that ‘in stock’ is a sort query and that blue is an attribute. Then it has to know that we’re only interested in Rochester dealers.
This kind of thing is why we have to be very wary of claims of accuracy in sentiment analysis. Unless a service is having actual humans read every result you can only use sentiment as a guide to the general direction of the discussion.
Reputation also varies with demographics and you can see some of this in SM2. If SM2 shows a majority of males from 34-50 in the Midwest think Obama is a Muslim (he is not!), then your management has identified a particular demo in social media that requires your attention and some remedial action.
Reputation management is labor and time intensive. It requires real time discovery because distortions can travel extremely fast in social media, the ultimate rumor mill. Like a recent Doonesbury storyline depicting his weary daughter relentlessly scanning the web 24/7 for Obama smears, it requires a lot of attention.
ROI for Reputation Management?
How do you measure the cost of swing voters in a hotly contested state? Of a false product rumor that derails sales overnight? Of not being prepared when a new market sector latches onto your product for a use you never considered? The ROI is based on risks averted which is tough to quantify.