Class lectures for Comm 399: Fundamentals of Social Media, Fall 2012, Department of Communication, Shepherd University.

1. Influencers! Old is new again
Professor Matthew Kushin, PhD
Shepherd University | Department of Mass Communication | 2012

2. I. Media’s limited effects:
“The People’s Choice”

3. Paul Lazarsfeld
 Studied election campaign of 1940
 Erie County, Ohio
 Field Experiment
 1-time In-person Interview
 Panels
 Repeated interviews May - Nov

4. Paul Lazarsfeld
 Focus:
 Changes in voting decisions among public
 What causes those changes?
 What’s the media’s role in them?

5. Lazarsfeld’s 4 Types of changes
 Early Deciders
 Chose a candidate in May & never changed mind.

6. 4 Types of Changes
 Waverers
 Chose 1 candidate then became undecided or switched, but
came back to initial choice when voting

7. 4 Types of Changes
 Converts
 Chose 1 candidate but switched to vote for opposing candidate

8. 4 Types of Changes
 Crystallizers
 Had no candidate in May but decided by November
 Voting choice was predictable based on characteristics
 Religion
 Where they lived
 Political party affiliation

9. Hypotheses
 If media has strong effects, Lazarsfeld could hypothesize:
 H1: Most voters will be influenced by media causing them to be
wavers or converts
 H2: People who changed the most should have been greatest
users of media

10. Study’s Findings
 Most people never changed opinions
 Early Deciders & Crystalizers
 Those who changed their minds were not influenced by
media
 Low media use
 Report being influenced by others

11. Study’s Conclusion
 Media’s main role:
 Reinforce a voter’s candidate choice
 Activate predispositions
 Party loyalties, political ideologies, religious affiliations

12. What is the possible connection here?
 Heavy media users tended to be:
 Early-deciders
 Light media users
 Heavily reliant on others for information
 Late deciders or converts

13. Connection
 Hypothesized:
 Maybe the heavy users are the same people who the more
apathetic voters relied on
 These heavy users may be knowledgeable & respected people the
apathetic voter looks up to
 Rather than being influenced by media, the heavy user gains
knowledge as ammunition to:
 back up their own opinion
 Influence others

14. II. Two-Step Flow

15. Two-Step Flow
 Messages pass from media to leaders.
 From leaders to many followers.
Opinion Opinion
Leaders Followers

16. Two Step Flow
 Opinion leaders and followers are alike in their social status
 Opinion leaders – more socially active, greater media
users, gregarious

17. Influencers: “Brand Evangelists”
 Not everyone on social media is equal in terms of influence

18. Two Step
This Idea?
Not New!
Opinion Leaders
Opinion Opinion
Opinion
Follower Follower
Follower

19. Diffusion of Innovation
 Like the 2-step flow
 Mass Media – Primary role to create awareness and knowledge about
innovation
 Interpersonal networks –Decision to adopt is highly influenced by
discussion w/ peers who already adopted or rejected innovation.

20. Diffusion
 Asks:
 Why do some new ideas become adopted and others don’t?

21. Diffusion
 Focus:
 How new information becomes widely adopted
 States:
 New ideas pass through a set of predictable stages

22. Diffusion Stages
 Media makes people aware of new idea
 Early adopters adopt innovation
 Early adopters influence opinion leaders who adopt
innovation
 Opinion leaders influence opinion followers
 Almost everyone has adopted
 Some people lag behind

23. 5 Adopter Types
 Classification of individuals by rate of adoption:
 Innovators – venturesome; eager to try new ideas
 Early adopters – respectable localites; high degree of opinion
leadership within social system
 Early majority – interact frequently w/ peers but seldom hold
leadership positions
 (Cont’d next slide)

24.  Late majority – skeptical; often adopt because of economic
need or pressure from others
 Laggards – Resist/reject. Oriented to past & tradition.

25. Typical Adopter Curve

26. Diffusion over Time
Innovation
take-off

27. Change Agents
 professional who attempts to influence adoption decisions in
a direction he/she feels desirable
 Influences:
 Early adopters
 Opinion leaders
 PR folks are 1 type of change
agent

28. “Birds of a feather”
 Degree to which pairs of individuals who interact are:
 Homophily - similar in certain attributes
 High homophily b/w opinion leader & followers
 Heterophily –different
 High heterophily between change agent and potential adopters

29. Path
 Change agents:
 Often use local opinion leaders to assist in diffusion:
 to bridge heterophily gap
 using opinion leaders wide influence over others
Change Agent or
Mass Media
heterophily
Opinion Leader
homophily
Like- Like-
Like-
minded minded
minded
Follower Follower
Follower

30. Digital Influence

31. What’s your score?
 If you have a Klout, Kred, or PeerIndex score – load it up so
we can see it!
 If you don’t:
 Goto: Tweetreach.com and type in your address.

32. What is digital influence?
 1. Reach: The number of followers online
 2. Voice: Number of posts in a given time period
 3. Calculated as a score, such as on Klout.com
 4. Number of interactions with others on social media.
 5. Something immeasurable.

33. Influence?
 What did you find?
 What does it mean?
 If you don’t know – take a minute and see if you can find
information on how the score is calculated or what the statistic
on tweetreach.com means.

34. Kred’s ‘algorithm’
 “Every person or account on Twitter has a Kred score, which is
made up of two parts: the influence score and the outreach score.
Your influence score is a measure of your ability to inspire others. It
is a number on a scale from 1 to 1,000, and is based on how often
your tweets are retweeted, how many new followers you are
gaining, and how many replies you generate. (Kred also looks at
Facebook likes and Google +1s, but Twitter is the main source of
data). It is very much like your Klout score. The Outreach score is
measured in levels and is a reflection of how generous you are with
retweeting and replying to others.
 Kred also figures out which of 200 communities you belong to
based on the information in your Twitter bio (which is not always a
great description of who you are). It can show you the influence of
your whole community and how you rank in that community”

35. Can digital influence be scored?
 Brian Solis states “Influence is not popularity and popularity is
not influence.”
 Sites like “Klout” are controversial.
 People game these sites to increase their scores.
 PR people chase false influencers, wasting time!

36. Digital Diffusion
 Innovations diffuse in our highly networked world today via
social media.
 Problem:
 How influence occurs through these online channels is debated,
often unknown, and people are trying to make $ selling a
‘formula’ it to us 

37. Looking Forward
 Next class we’ll start looking at digital influence
 How to find influencers (if its possible!)
 And explore what to do once we find them!