1) The document discusses how peer-to-peer (P2P) communication and mass broadcasting media interact to influence the spread of information and opinions among social networks. 2) Through simulations, it finds that P2P communication over scale-free networks can inhibit the effects of false information spread through mass broadcasting, as long as individuals' tolerance for differing opinions is reasonable. 3) P2P communication allows for self-confidence to act as a filter for information, slowing the invasion of "informational lemons" spread through mass broadcasting until it reaches about 40% of the audience.

1. Turning Information Into
Knowledge:
The Role of P2P Communication
Walter Quattrociocchi°*
Rosaria Conte°
Elena Lodi*
°LABSS/ISTC-CNR
*Science Dept. University of Siena
Kassel WCSS’10

2. Premise 1/2
• Current simulation models of opinion dynamics (Duffuant et
el.,2001; 2002; Hegselmann and Krause, 2002) are based
on Social Impact Theory (Latané 1981; Nowak et al., 1990),
where influence
is said to depend on
distance, number, and strength (i.e.,
persuasiveness) of sources.
• Simulation-based studies of opinion dynamics observe how
opinions spread and aggregate as a function of the
distance among values assigned to them.
• But we know that social structures influence opinions,
more or less steadily

3. Premise 2/2
• Informational influence
(since Sherif, 1936) under
ambiguous stimuli
 Agenda setting theory
(McCombs and Shaw,
1972): correlation between
frequency of information
delivered by media and
social perceptions.
 Let us see a recent
confirmation of this theory.

4. A case study.
Effect of media in the last Italian
political campaign
(reproduced from Diamanti, 2008)

5. Hence
 When speaking about opinion As in bounded confidence model
dynamics we must take agents’ (Deffuant et al),
• opinions are numerically defined
confidence into account (certainty)
• agents exchange opinions based
on the distance between their
values: they adjust opinions only
if preceding and received
+ information are close enough,
modelled by introducing a
parameter t for tolerance above
which opinions are resistant to
change.
 social scientists’ intuition and Agents are exposed to different
evidence gathered, that entities and force with variable
landscape of social influence influence
Receive inputs from distinct
is far from flat! sources of information

6. Research
questions
• In this paper we intend to explore the impact of different
interacting communication systems and information
sources on information quality
• Correlation between frequency of information delivered by
media and social perceptions?
• Does peer-to-peer communication amplify effects of old
media, or exercise independent influence

7. Preliminary definitions
• Scale Free Network
• Agents are connected in a scale free network, in which
nodes are progressively added by introducing links to
the existing nodes on a “preferential attachment”
schema.
• The construction strategy of the algorithm aims at
maintaining the link probability between any couple of
nodes proportional to the number of existing links
already connected to the selected node.
• Bounded Confidence Model (BDM):
• Agents mix their opinions when differences is smaller
than threshold. More precisely…

8. The media
Consider the set
• ml , mr represent values of events related to
welfare and security issues reported on by the
media
• V1 is the subset of agents that receive information
from the central media.

9. Interacting peers
Agents’ preferences are set by a uniform random
distribution.
Interacting peers, v V, are nodes of the Social
Network
The more the agent’s opinions - with respect to
welfare and security - vl and vr approximate 1, the
more each issue is important for the agent.

10. P2P interaction
• After broadcasting, each agent communicates with
neighbors within a distance set to 1.
• Following BCM convention if the difference between
two agents’ opinions - respectively represented by x
and xi - is lower than threshold (x−xi < t) these
opinions will be mixed by applying:

11. Information quality
• To reproduce Italian central media in the
last political campaign, media are
assumed to deliver false information.
• The closer a reported information is to the
opposite information spread by central
networks, the higher its quality.

12. Interaction
between
media and peers
• Peers acquire information from media according to a
passive protocol, by acquiring the values they send and
comparing them with their previous preferences.
• Information is accepted or not, based on bounded
confidence mechanism.
• The agent’s preferences vl , vr and the information from the
media mld , mrd are transformed in two new agent’s
preferences. The function generates two new values for vl ,
vr.
• t stands for peer agents’ tolerance, i.e., subjective
disposition to accept others’ information. The higher the
value of t, the higher the agent’s disposition to accept
others’ inputs.

13. • Baseline experiment with nine scenarios, with
number of agents set to 100, no media
broadcasting and increasing levels of tolerance
Baseline
•
(from 0.1 to 0.9 at step 0.1) for 100 turns.
Simulation is performed 10 times per scenario,
results
and results are averaged.
At beginning opinions
(welfare and security)
are set up randomly
within
interval ]0, 1[: both
opinions fluctuate
around average value
of initial distribution,
meaning that, over a
scale free network,
P2P communication
leads to a flat
distribution of
opinions.

14. How about mixed communicatiion?
• How do they interact? In
Broadcast
particular, does P2P
communication amplify or inhibit
and P2P
the effect of central media?

15. Conclusions
• MB drives opinions by steering information among agents. It represents a
fundamental medium for knowledge diffusion.
• The wider the audience reached by the broadcasting system, the stronger
its influence especially when people are poorly self-confident and more
likely to accept incoming information. False information spreads fast and
easy.
• Is there any way to contrast such an influence?
• Peer-to-peer communication over a scale-free network can inhibit and
slower invasion of informational lemons.
• With reasonable level of tolerance, P2P communication inhibits
effects of MB until this has reached the 40% of the audience.
• P2P communication, thanks to reciprocation, allows self-confidence
to act as an efficient filter of information.

16. Next
 Level of confidence is not enough: effect of other types of
mentall states on opinion dynamics (beliefs, doctrines,
ideologies, ideals, etc.). What is an opinion? Whatʼs the
difference from beliefs?
 More sophisticated mechanisms of belief/opinion formation,
revision and transmission, focusing on oneʼs representations of
other s beliefs;
 Different types of P2P communication (for example, reporting
oneʼs Vs othersʼ beliefs);
 Wise agents, i.e. a subset of agents having direct access to
knowledge
 Social structuresʼ properties, possibly implementing one real-
world network, and checking effects of speed in P2P
communication.

17. Thank you!!!