Spatial social media response to live events. The Milano Fashion Week case. Presentation at LocWeb

Brambilla, Ceri, Daniel, Donetti. Spatial Analysis of Social Media Response to Live Events. The Case of the Milano Fashion Week

Spatial Social Media Response to Live Events

The Milano Fashion Week

Marco Brambilla, Stefano Ceri, Florian Daniel, Gianmarco Donetti

[email protected] marcobrambi LocWeb Workshop @ WWW 2017, Perth, Australia

Brambilla, Ceri, Daniel, Donetti. Spatial Analysis of Social Media Response to Live Events. The Case of the Milano Fashion WeekPope

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Research Questions

“Are live events still relevant?

• Can online visibility be described simply by how famous is the brand?

• Do space and time still matter?• Can we predict how people behave in time/space within

events?


Method

• Collection of domain knowledge from field experts• Extraction of social network content• Popularity, space and time analysis of brand impact• Correlation across dimensions• Prediction of visibility statistics


Domain Experts

Official calendar

Posts Posts Classified by Brand

query wrangling

Brands& events

Data storage

Data Acquisition Process


The DataIn fashion

Prominence of InstagramHigher geolocation percentage106k

Tweets

6.5%Geo-located

556kInstagrams

28%Geo-located


Spatial Response AnalysisAnalyze the social media response to the events with respect to the geographical location of posting

Within the city of the event (Milan)

Two types of signals:1. The official calendar events2. The geographical positioning of posts

of social media (geolocated ones)


Spatial Response Analysis1 week before the eventThe week of the event1 week after the event

Blue dots: social media postsRed stars: physical locations of shows

[Day by day animation]


Spatial Response AnalysisMethod:

Grid over the cityDensity of posts in cells

Measures:Gini coefficient# of alive cells (1%+ posts)# of Active cells (10%+ posts)# of strongly active cells (20%+ posts)Distance of posts vs. event location


Spatial Response AnalysisTemporal granularity:

Within 3h, 6h, 24h, and whole period


Spatial Response Analysis

42 features describing geographical behaviour of each brand.

OVERALL: SINGLE BRAND:


All brands – Geo responseK-Means clustering result

Spatial response analysisYellow, with low average distance, the most concentratedRed, with average distances slightly higherGreen, with the highest results for average distance, the most dispersedBlue, a single element with highest number of alive cells

K-means clustering in a PC85-dimensional space


Time response analysisAnalyze the social media response to the events with respect to the axis of time

2 types of signals:1. The official calendar events2. The volume of posts on

social media

VersaceNumber of posts: 4358 on Instagram, 389 on Twitter


Time response analysisStudy the causality relationship between the events calendar signal and the social media signal through a series of Granger Causality tests

VersaceNumber of posts: 4358 on Instagram, 389 on Twitter

VersaceGrange Causality tests result

GRANGER CAUSALITY TEST


Time response analysis


Time response analysis

K-means clustering

All brands – Granger Causality tests resultK-Means clustering – 4 Cluster Centroids


Clustered Time response analysisAll brands

Granger Causality tests resultAll brands – Granger Causality tests resultK-Means clustering – 4 Cluster Centroids

Yellow, with high immediate responseRed, with response peak delayed at 15 minutesGreen, with delayed response peak at 45-60 minutes Blue, with mild initial response and a 3 hours lagged response peak


Time Response PredictionBuild a prediction model for the cluster membership of each brand based on the Granger tests results, given features related to the events and brands


Time Response PredictionRandom Forest: accuracy close to 62%, but only a few “really” wrong.

All brands – Granger Causality tests result

Ground Truth from K-Means clustering

All brands – Granger Causality tests result

Prediction of the cluster membership


All brands – Popularity responseK-Means clustering result

Brand Popularity ClusteringFeatures:1. # Posts on Instagram2. # Likes on Instagram3. # Comments on Instagram4. # Tweets5. # Likes on Twitter6. # Retweets

K-means clustering


Cluster comparison Accuracy = 44.6%

Spatial clustering

Temporal clustering


Cluster comparisonAccuracy = 41.5%

Popularity clustering

Temporal clustering


Cluster comparison

Spatial clustering

Popularity clustering


Comparing the different dimensions

Comparison Accuracy

Time VS Space 44.6%

Time VS Pop 41.5%

Space VS Pop 38.6%

Time

Space

Popularity

CONFUSION MATRIX:

Tim

e clu

ster

s

Space clusters


Conclusions ChallengesA complete and well-defined brand categorization is possible only taking into account all three dimensions:

PopularitySpace Time

• Imprecise geo-positioning• Re-posts• Coverage of non-geotagged

content• User profile• Other posts from same user• Content analysis (e.g., image

similarity)• Social science and behavioural

aspects


Spatial Analysis of Social Media Response to Live Events.The Milano Fashion Week

Marco Brambilla @marcobrambi [email protected]://datascience.deib.polimi.it http://home.deib.polimi.it/marcobrambi

THANKS! QUESTIONS?

http://datascience.deib.polimi.it/

http://home.deib.polimi.it/marcobrambi

http://home.deib.polimi.it/marcobrambi

Spatial social media response to live events. The Milano Fashion Week case. Presentation at LocWeb

Data & Analytics

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