The document discusses and compares two paradigms for how trends spread in online social media: the influential hypothesis and the crowd hypothesis. The influential hypothesis suggests that a small number of "opinion leaders" drive the adoption of new trends, acting as intermediaries between the media and the public. However, the document argues this view is outdated and does not apply well to today's internet era. Instead, evidence shows the crowd's early participation in spreading ideas can lead to widespread diffusion, rather than domination by influential elites. Analysis of slang word adoption over time supports the crowd hypothesis, finding ordinary users with around 150-300 followers play a key role in trends going viral.

1. Who creates trends in online
social media.
The crowd or opinion leaders?
Leihan Zhang, Jichang Zhao and Ke Xu1

2. Amir Razmjou
 Computer Science Student
(MSc)
 Azad University of Mahshahr
(2007)
 Business Intelligence
Developer
 Microsoft CRM
 WISP – Firewall Developer
 Microchip Embedded
Programmer
Brief Introduction
Swarm Intelligence
Network Security
Cloud Infrastructure
Complex Networks

3. Influential hypothesis (Past)
In the 1940s and 1950s, Paul Lazarsfeld, Elihu Katz, and colleagues (Katz and Lazarsfeld
1955; Lazarsfeld, Berelson, and Gaudet 1968) formulated a breakthrough theory of
public opinion formation
Information Diffusion? (Now)
We are going to challenge Influential hypothesis by
comparison of two Paradigms in two different eras

4. Influential hypothesis
Small minority of “opinion leaders”– stars, act
as intermediaries between the mass media and
the majority of society (circles)
Two‐step flow
• The “dominant paradigm” of media
sociology in 70s
In business and marketing, the idea that a
small group of influential opinion leaders may
accelerate or block the adoption of a product
is central to a large number of studies
Individuals may be influenced more by exposure to each other than to the media
Duncan J. Watts 2007

5. Shortcomings of Influential hypothesis
• Most devoted to inspection of underlying mechanism not
considering the time dimension.
• It’s doesn’t belong to information age.
• The Internet
• Micro-Blogging
• While at the same time, the big-data of behavioral records in
online social media
• Lehmann: Not taking into account external factors, geographical
location of people, their mood.
• The traditional two-step flow theory is not applicable in online
social networks and the role of influential might be over-
emphasized
• High popularity do not always imply high influence but
relationship among ordinary users and the readiness of the social
network to accept a novel item
• Harrigan et al. also find that it is the network structure not hubs
that can substantially increase social the spreading of a message.
LOST IN TIME
AND SPACE

6. Frequency of slang words over time

7. Innovator
Early Majority
Early Adaptors
Late Majority
Laggard

9. • The jump in the occupation of users with of number of followers
around 10^5 in p1 represents the failed diffusion.
• crowd’s participation in the early stage of the propagation can produce a
massive diffusion. On the contrary, domination of opinion leaders in the
early stage cannot guarantee the
• All the results are averaged over the entire set of slang words
• How come that contribution of users decreases substantially after specific
number of followers?

11. How did we find the effective number followers? (~232)

12. It again demonstrates the fact that ordinary users occupy greater
proportion than opinion leaders for p2
The threshold we find here is close to Dunbar’s number
CDF curve of p2 exceeds the curve of p1 for small #Follower
It is in agreement with the existing hypothesis that a global trend is beginning from many small-
scale trends and the population number of an efficient group launching small-scale trends shall be
less than Dunbar’s number

13. Questions?