Rumors vs Confirmed News on Twitter

Twitter Under Crisis-Can we Trust what we re-Tweet?

Clement Robert Kalthoom

Introduction

● Twitter is a micro-blogging service ● brings together millions of users ● enables real-time propagation of information to a large

group of users● not only enables the effective broadcasting of valid

news, but also of baseless rumors.

Chilean earthquake● The Chilean earthquake on Saturday , February 27, 2010 at 06:34:14 UTC

(03:34:14 local time)● It reached a magnitude of 8.8 on the Richter scale and lasted for 90

seconds.● it is considered the seventh stronger earthquake ever recorded in history.

studies over post-quake

● Research questions: To analyze the impact of Twitter on the propagation

of information during the Chilean earthquake,

● perform two types of studies over post-quake tweet data:

○ characterize the usage and social networks of the days immediately

after the event. The goal of this task is to observe how rumors and

news are propagated and the dynamics of the followers/followees

relationship.

○ investigate the ability of the social network to discriminate between

false rumors and confirmed news. To do this we examine tweets related

to confirmed news and to rumors, classifying manually each tweet.

Twitter Network During an Emergency

● Experimental Framework○ Collection of Users Activities

(Feb 27-March 2, 2010)■ Tweets: Tweets with

keywords related to the event.

■ Users Locations: Users around Santiago Timezone

○ Tweets: 4,727,524 tweets: 19.8% are replies to others

Twitter Network During an Emergency

● The Social Network

○ # of users: 716,344

○ #Following>#Followers(49.6%)

○ #Following<#Followers(46.2%)

● Authority Users with>100k

followers= 633

○ Most of them are

politicians, Mass Media &

celebrities.

Twitter Network During an Emergency

● # of tweets per User

○ >50% have 1 tweet

○ 11.47% of users have >10

tweets each

● The Average # of tweets (6.5) >

the Median

○ Outlier→ Some were tweeting more

than expected

Twitter Network During an Emergency

● Influence of Authority on # of tweets

produced

○ When the # of tweets decreases, the # of

followers decreases

○ 50 top users have high value of

followers

○ Top users are following less # of users

○ Top users: Celebrities, Politicians,

Mass Media,etc

Twitter Network During an Emergency

● Relationship between Top Users

○ CNNBBreakingNews does not follow anyone in top-20 users

○ Mass Media + NGOs + Individuals

○ Active users are strongly connected

Twitter Activity During an Emergency

● Comparison of 2 Trends during

○ Music Festival vs Earthquake○ Festival trend disappeared 20

minutes after the event○ Nbr of the Earthquake increased

since then.

Vocabularies used during the event per day.

Differ depending on the social needs.

Twitter Activity During an Emergency

● Re-Tweets Propagation

○ Retweet activity shows

how social network helps

in information

propagation.

○ How deep re-tweets cover

the social graph

indicates the relevance

of the tweet for the

community.

FALSE RUMOR PROPAGATION

● Veracity of information on Twitter and how this information is spread through the social network

● Manually selected some relevant cases of valid news items, which were confirmed at some point by reliable sources.

● Manually selected important cases of baseless rumors which emerged during the crisis (confirmed to be false at some point).

Classification results for cases studied of confirmed truths and false rumors

1. Goal is to observe if users interact in a different manner when faced with these types of information.

2. Each case studied was selected according to the following criteria:

a. Asignificant volume of tweets is related to the case(close to 1,000 or more).

b. Reliable sources (external to Twitter) allow to asses if the claim is true or false.

Classification of results for cases studied of confirmed truths and false rumors● These results show that the propagation of tweets that correspond to rumors differs

from tweets that spread news because rumors tend to be questioned more than news by

the Twitter community.

● This fact suggests that the Twitter community works like a collaborative filter of

information.

● This result suggests also a very promising research

line:

○ it could possible to detect rumors

by using aggregate analysis on tweets?

Conclusion and Lesson Learnt● +

○ Social Networks Analysis revealed interesting facts:■ Rumors are those tweets with Many Questions and Denies■ Confirmed News get Many Affirms.

■ Twitter Community works like a Collaborative filter of information

● - ○ No automatic way to classify confirmed news against false rumors

■ If it was there, would it be able of analysing facts like sentiments, sarcasm etc?

■ And Label tweets as Questionable or Not.○ Social Side of the Issue vs Social Community on Twitter

■ Who’s in a position to confirm news?

● Tsunami was real but, Government officials had denied its existence!

Thank you