Nisha Ranga DYNAMICS OF CONVERSATION. INTRODUCTION PRELIMINARIES PROPERTIES OF CONVERSATIONS Size and Depth Degree Authorship MODELS BP-Model T-Model.

Nisha Ranga

DYNAMICS OF CONVERSATION

OBJECTIVE• INTRODUCTION

• PRELIMINARIES

• PROPERTIES OF CONVERSATIONS

• Size and Depth

• Degree

• Authorship

• MODELS

• BP-Model

• T-Model

• Mixture Model

INTRODUCTION

Analyze the structure of conversations :

• Usenet Groups

• Yahoo! Groups

• Twitter

DataSet consists of

• ID of message

• ID of parent message

• Author of the message

• Timestamp

INTRODUCTION

• How do online conversations build?

• What similarities and difference can be observed between different groups?

• Is there any model that human communication follows?

PRELIMINARIES

• Propose a simple mathematical model for the structure of conversations

• Account for factors such as recency and author identity that may affect conversations.

• Compare the predictions of these models back to the empirical data for three datasets: Usenet groups, Yahoo! Groups, and Twitter

NOTATIONS

• Denote messages by letter u, v, w…

• Messages are assumed to have a thread structure

• A message with no children is a leaf message

• A message with no parent is the root message

• t(u) is a timestamp of message u

• The messages in a thread are created chronologically

• If ‘u’ is a parent of ‘v’, then t(u) <= t(v)

THREAD

Def: The root message, along with its descendants form a connected component which is called a thread

PROPERTIES OF CONVERSATION

• SIZE AND DEPTH OF THREAD• Depth: length of the maximum path from the root to a leaf in a thread• Size is roughly quadratic to depth

PROPERTIES OF CONVERSATION

• DEGREE OF A THREAD

• Degree distribution is closer to power law i.e. p(k) k- for some >2

• Degree distribution is not independent of the level of a thread

• If root is at level 1, then degree distribution becomes ‘steeper’ with the level as having more children becomes less likely at higher levels

PROPERTIES OF CONVERSATION

• AUTHORSHIP

• There is a polynomial relationship between the size of a thread and the number of authors participating in the tread

• The author A(u) of a message u is the person who wrote it

• A single person can author multiple messages in a thread

BRANCHING PROCESS MODEL(BP-MODEL)

• Each thread starts with a root node

• At the ith level of the thread constructed each node generates a certain number of children according to the distribution p

• p(k) is a probability of a leaf u to have k children

• The process terminates when there are no more children

• Let be the random variable denoting the number of children at 𝑍𝑖the th level, then𝑖

𝑍 = ∑ 𝑖 𝑍

Where Z denotes the size of the thread

DRAWBACK OF BP-MODEL

• The model is not generative, i.e., the degree distribution is stipulated

• This model cannot capture the depth distributions of threads that are observed in reality

• In the branching process model, the number of children at each node is determined by a single distribution

• The branching process model does not capture the order in which the messages are created, i.e., the timestamps associated with the messages are left out

• It does not capture the author of messages

T-MODEL

• Threads grows in a discrete time steps

• Either a thread is stopped i.e no more messages are added

• A message is posted in response to the current message v

• Current degree of v – degv

• Recency of v – rv

• h(degv, rv) = degv+rv for constants >=0 and (0,1)

• Thus, both degree and recency play a role in generating different types of threads

• If degree plays a major role then the tread is bushy

• If recency plays a major role then thread is skinny

TI- MODEL• This model is used for author identity

• Author tends to respond to responses to their own earlier messages

• “Identity copying” effect

• New message v arrives with u=parent(v)

GROUP

fa.linux.kernel 0.98

uk.politics.electoral 0.98

rec.arts.drwho 0.97

uk.politics.crime 0.97

chile.soc.politica 0.96

USENET

GROUP It.discussioni.leggende.metropolitane 10

It.politica.polo 10

Rec.games.chess.politics 3

Bln.politik.rassismus 2

Sk.politics 1.5

Preferential behavior:Highest degree of preferential attachment is shown below

Recency behavior:Higher recency effect is shown below

USENET• IDENTITY “COPYING”

• High (low copying rate) indicates new authors tend to join in often

• Low (high copying rate) indicates tendency for authors of posts to have previously already authored a post

High (low copying rate): or.politics

alt.fan.cecil-adams

alt.marketplace.online.ebay

pl.misc.kolej

rec.arts.sf.written

Low (high copying rate) linux.debian.bugs.dist

microsoft.public.excel.misc

microsoft.public.excel.programming

nctu.talk

tw.bbs.campus.nctu

YAHOO! GROUPS• Groups with high degree of preferential attachment () and high

recency effect ()

Group

indianmedical =10

IllinoisSpeakers

DetectiveRichardHead

Bodybuildersaverageguys

villageDesign

NorthCarolinaSpeakers =0.99

stbaseliosorthodoxchurch

LostnFoundEvents

PatriceVinci

molecular-biology-notebook

TWITTER

Group

#mustsee =10

#twitterinreallife

#readingrainbow

#whathappenswhen

#vogueevolution

#yankees =0.99

#warriors

#tiff09

#iranelectioni

#followfriday

The first set corresponds to highest () which means bushy thread and the second set corresponds to highest() which means skinny thread