1 www.stfc.ac.uk

2

Type of Networks based on Size

Social Networks

WWW Networks, Telecom

Networks

Email Networks

Organisational Networks, Chemical Networks,

Sports Networks

Criminal Networks, Terrorist Networks

Economic Networks, BoD

Networks, Lobby Networks, Citation,

Patents

Friendship Networks Team Networks Migration Networks Refugee Networks Micro

Meso

Macro

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Network Influencer Identification

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Finding:

Evidence:

Enterprise Implications:

4

Based on analysis of Social media chatter around Olympics, we found that Gymnastics is the most popular sport, followed by cycling, basketball, swimming, equestrian, rowing, archery and judo.

• A sample of 43,048 tweets across 27,683 users were collected between July 29th and July 31st.

• Concentration across the US, Europe and Australia.

Popularity of games & sports such as Gymnastics, Swimming, Equestrian, Rowing, Archery and Judo is an opportunity for corporates to associate their brands with them.

Brand associations

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Finding:

Evidence:

Implications:

5

Institutional investors investing in Indian market have large exposures to a few blue-chip stocks.

• Normally one would associate this behavior at the level of individual investors.

• The institutional investors also depict a low risk investment behavior as exhibited in their large exposure to blue-chips stocks.

• This suggest the presence of Bandwagon effect, Peer influence, Conformity and Contagion in the investment behavior of institutional investors.

Contagion effect in Mutual Funds operating in India

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Board of Directors: Top 100 most valuable companies in India by market capitalization

46 clusters. The figure shows the clusters and their interconnections through board memberships. The links

show board membership ties between the members. This could be applied to, for example, predicting voting

patterns

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State of the Union messages

Most influential contexts: #0: american people nation future #1: congress good million agreement #2: year america enemy state #3: iraq terrorist iraqi force

Most influential contexts: #0: american people bank country #1: year economy crisis energy #2: america time make large #3: job million create industry

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Social Media & The Tale of Two Storms:

Hurricane Sandy & Cyclone Nilam

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• Social Media sources have played a key role in disaster reporting, relief and rescue efforts.

• Social media destinations such as Twitter, Flickr and Facebook were leveraged extensively to spread information during the Victorian bushfires in Australia in 2009.

• We try to explore the role played by government, civic authorities, law and order, general public, celebrities, activists, journalists and media in the advent of two natural disaster situations: Hurricane Sandy and Cyclone Nilam.

Overview

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• Hurricane Sandy was the largest Atlantic hurricane on record, as well as the second costliest Atlantic hurricane only surpassed by Hurricane Katrina in 2005.

– Hurricane Sandy struck in late October 2012. In the United States, Sandy caused severe damage in New Jersey and New York.

– It claimed more than 50 lives, left millions without power and caused over US$ 50 billion in damage in the United States.

– Damages to life and property are spread across Jamaica, Haiti, Dominican Republic, Puerto Rico, Cuba and The Bahamas.

• Cyclone Storm Nilam, which struck India in late October 2012 caused damages across Indian states such as Tamil Nadu, Andhra Pradesh, Karnataka and Odisha.

Sandy & Nilam

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• According to Semiocast, the United States has 141.8 million Twitter users, and India has over 15 million Twitter users

• We collected tweets containing the hashtag #Sandy from Oct 30th until Nov 6th (500,036 tweets from 306,348 users) – We classified #Sandy tweets into four different sets based on the time interval when the tweets

occurred

– Time Interval 1 has 180,489 Nodes and 232,578 Edges

– Time Interval 2 has 60,351 Nodes and 65,842 Edges

– Time Interval 3 has 31,033 Nodes and 31,039 Edges

– Time Interval 4 has 14,550 Nodes and 13,350 Edges

• We also collected about 1500 tweets with the hashtag #Nilam during 31st Oct 2012.

The Data

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Hurricane Sandy: Time Interval 1

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Hurricane Sandy: Time Interval 2

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Hurricane Sandy: Time Interval 3 & 4

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Hurricane Sandy

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Centrality Measures

Betweenness Centrality

Time 1 Time 2 Time 3 Time 4 azipaybarah chipsandpickles diannank janedoenut

Occuweather PSteely mzelma stevenacook

NYGovCuomo Shabbyginger MAMIVERSE DyckmanBar

buell003 EFF rootlessme AsBestRecipes

Newyorkist ShaneFloyd FlourMagic urbanjibaro

nycarecs NYCFUNRIDE proudamericans MarianaByDesign

DanielSquadron ginjula julienegrin JeffreyKyle

NYDNHammond briandonohue SOYLAMAR JohnGaltReport

NewYorkPost FreeUnivNYC sunshineejc MoScarlet

SallyGold sanjayguptaCNN dianalimongi DorDor29

adamlisberg NYeHealth MYERTECH tonytorero

MTAInsider GOOD CocktailBodega krys730

fema 923NowFM LarisaLive tinackp

nowthisnews Agent350 Romniac sookietex

AntDeRosa sloane GoodwillRescue Sharpie_Says

sahnetaeter csanati Laura_Byrnes PalanteLatino

nydailynews PhunkyLondon 32BJ_SEIU metnyc

YourAnonNews SenatorMenendez ElisaBatista lafamiliacool

OccupyWallSt NYHealthScape peterfhart GinnyMackles

Ows_Casper TimGuinee crampell unitedcommon

Degree

Time 1 Time 2 Time 3 Time 4 FrancisBoulle NYGovCuomo NYGovCuomo TheRealXtina

MINDBLOWlNG FrancisBoulle Alyssa_Milano NYGovCuomo

EmWatson whateverson CarrieFairygirl YourAnonNews

NYGovCuomo sanjayguptaCNN essamz RBPundit

realDonaldTrump AC360 chrisrockozfan fema

MindbIowingFact MTAInsider ConEdison AKRPR

NewYorkPost MINDBLOWlNG MINDBLOWlNG halfadams

nowthisnews RealMindBlowing anamariecox RedCross

kurtdietrich morrowchris FrancisBoulle AntDeRosa

katespencer ReaderMom71 SteveNiles EmWatson

YourAnonNews ERogTweets2Much HuffingtonPost sophieraworth

ArianaGrande 9GAG BW realjeffreyross

bananarams NathanTheWanted EmWatson Gothamist

CrazyHoneyBooBo realDonaldTrump AKRPR GovChristie

RealMindBlowing MindbIowingFact BarackObama andersoncooper

NathanTheWanted EmWatson RedCross BarackObama

JimGaffigan BarackObama MikeBloomberg WNYC

iamdiddy alexanderludwig fema Janedoenut

AnthonyShaw_ EndHateRadio GovChristie alexa_chung

TheRealNickMara NewYorker Gizmodo Chrisrockozfan

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• Sandy was 'Instagrammed': Number of #Sandy tweets originating from Instagram ranked fourth, preceded only by Tweets originating from iPhone, Web and Android and in the case of Nilam, Twitpic scored high.

– In disaster scenarios, visual means of message propagation assume prominence.

– This is in contrast to our observations with regard to message propagation during the Olympic Games, where Tweets originating from Instagram scored very low

• Government and officials leveraging Social media: @NYGovCuomo, which is the Official Twitter account for the Governor of New York State, Mr. Andrew Cuomo consistently emerged as one of the most important entities. No such trends in the case of Cyclone Sandy where we did not see even a single tweet from a government official or utility.

• News Media leveraging Social Media: Journalists and News Media extensively leveraged Social Media for news propagation and for news amplification. News Media sources and Journalists were the heaviest users of Social media in the events of both Sandy and Nilam.

• Individual Journalists leveraged their Twitter follower base to spread messages faster and they invariably figured higher up in the network centrality measures as against the twitter accounts of Media houses which employ them.

Conclusions

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• Cyclone Nilam saw a low overall coverage in Social Media in India and surrounding affected regions.

• Celebrities in the United States were active in social media during Hurricane Sandy as against celebrities in India who chose to ignore Cyclone Nilam.

• Measures of centrality, can show who creates the most connections in the network.

– In our case, the Twitter handles of NYC Guv and several leading journalists play this role

• Lists of words frequently used in the network of those who mention #sandy & #nilam can be clustered by their closest affiliations and most commonly used words

– This can help trace communities within the network formed by the recurring use of particular words

• Using the URLs used in Tweets from the entire network as well as from each of the smaller groups within the network, we can trace a pattern of interests, values, and engagement that drew the most attention across the network.

Conclusions

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Thank you

jai3.ganesh3@Cognizant.com