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Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial and Temporal Analysis of Social Media Response
to Live Events: The Milano Fashion Week
Marco Brambilla, Stefano Ceri, Florian Daniel, Gianmarco Donetti
marco.brambilla@polimi.it
marcobrambi
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
PopeElection
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
PopeElection
Guess what
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
PopeElection
Guess what
He gets elected anyway
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Concerts?
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Fashion?
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Gente guarda cell in fashion
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Gente guarda cell in fashion
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Research Questions
“Are live events still relevant?
• Can online visibility be described simply by how famous is the
brand?
• Does physical participation still matter?
• Can we predict how brands behave within events?
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live 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 category of a brand
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Domain
Experts
Official
calendar
Posts Posts Classified
by Brand
query wrangling
Brands& events
Data storage
Data Acquisition Process
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
The Data
In fashion
Prominence of Instagram
Higher geolocation share
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial Response in Time
Analyze 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 events
2. The geographical positioning and
timestamping of posts of social media
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial Response in Time
1 week before the event
The week of the event
1 week after the event
Blue dots: social media posts
Red stars: physical locations of shows
[Day by day animation]
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial Response in Time –
Grid and Metrics
Method:
Grid over the city
Density of posts in cells
Measures:
Gini coefficient
# of alive, active and strongly active cells
Average distance of posts vs. event location
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Gini coefficient
The Gini coefficient is a measure of statistical dispersion
The Gini coefficient measures the inequality among values of a frequency distribution:
Gini coefficient = 0
Perfect equality, where all values are the same (e.g., where every cell has the same number of posts)
Gini coefficient = 1
Maximal inequality among values (e.g., where only one cell has all the social media activity, and all others have
no posts geo-located inside, the Gini coefficient will be very nearly one).
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Gini coefficient
We compute the Gini coefficient:
1. Over the entire grid over the Milano area
2. Over only those cells that results alive for at least one brand in the
specific frame of analysis
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Alive, Active and Strong cells
Three types of cells:
ALIVE: a cell with more than 1% of the total number of posts
ACTIVE: a cell with more than 10% of the total number of posts
STRONG: a cell with more than 20% of the total number of posts
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Alive, Active and Strong cells
And the difference between subsequent frames (e.g. 3h -> 6h)
in terms of on/off for each specific type of cell
3 hours 6 hours
2 on
1 off
ALIVE cells
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Average distance
Compute the average distance of the posts from the events, in terms
of cells, for each brand b, in the following way:
1
𝑛°𝑜𝑓𝑃𝑜𝑠𝑡(𝑔𝑟𝑖𝑑)
𝑐𝑒𝑙𝑙∈𝑔𝑟𝑖𝑑
𝑛°𝑜𝑓𝑃𝑜𝑠𝑡 𝑐𝑒𝑙𝑙 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑐𝑒𝑙𝑙, 𝑒𝑣𝑒𝑛𝑡. 𝑐𝑒𝑙𝑙)
Using Manhattan distance for the cell grid
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial Response in Time –
Slots and Characterization
Temporal granularity:
3h,
6h,
24h,
whole analysis period
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
All brands – Geo response
K-Means clustering result
Spatial Response in Time - Clustering
Yellow, with low average distance, the
most concentrated
Red, with average distances slightly
higher
Green, with the highest results for
average distance, the most dispersed
Blue, a single element with highest
number of alive cells
K-means clustering in a PC85-dimensional space
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Response Volume in Time
Analyze the social media
response to the events with
respect to the axis of time
Two types of signals:
1. The official calendar events
2. The volume of posts on
social media
Versace
Number of posts: 4358 on Instagram, 389 on Twitter
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Time Response – (pseudo) Causality
Study the causality
relationship between the
events calendar signal and the
social media signal through a
series of Granger Causality
tests
Versace
Number of posts: 4358 on Instagram, 389 on Twitter
Versace
Grange Causality tests result
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Time Response – Causality Curves
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Causality Curves Clustering
K-means
clustering
All brands – Granger Causality tests result
K-Means clustering – 4 Cluster Centroids
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Clustered Temporal Causality
All brands
Granger Causality tests result
All brands – Granger Causality tests result
K-Means clustering – 4 Cluster Centroids
Yellow, with high immediate
response
Red, with response peak
delayed at 15 minutes
Green, with delayed response
peak at 45-60 minutes
Blue, with mild initial response
and a 3 hours lagged response
peak
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Time Response Prediction
Prediction model for the cluster membership of a brand
based on the Granger tests results
given features related to the events and brands
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Time Response Prediction
Random 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
Predictionof the cluster membership
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
All brands – Popularity response
K-Means clustering result
Brand Popularity Clustering
Features:
1. # Posts on Instagram
2. # Likes on Instagram
3. # Comments on Instagram
4. # Tweets
5. # Likes on Twitter
6. # Retweets
K-means
clustering
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Cluster comparison
Accuracy = 44.6%
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Cluster comparison
Accuracy = 41.5%
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Cluster comparison
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Clustering Comparison
5 Principal Components
The Confusion Matrix
regarding the alignment of
Time Response vs .
Geo Response
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Comparing the different dimensions
A complete and well-defined
brand categorization is possible
only taking into account all three
dimensions:
Popularity
Space
Time
Comparison Accuracy
Time VS Space 44.6%
Time VS Pop 41.5%
Space VS Pop 38.6%
Time
SpacePopularity
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Concluding…
A method for comparing popularity, temporal and geographical response to
brands during live events
Limitations: actual presence of posting users is not granted; time and
space of posts may not be aligned (e.g., delayed posts; see design
scenario: 70% delayed posts)
Future work: deep understanding of content for determining physical
presence; data fusion with app, IoT, cell phone network
Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events.
Spatial and Temporal Analysis of Social Media Response to Live Events.
The Milano Fashion Week
Marco Brambilla @marcobrambi marco.brambilla@polimi.it
http://datascience.deib.polimi.it http://home.deib.polimi.it/marcobrambi
THANKS!
QUESTIONS?

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Spatial and Temporal Analysis of Social Media Response to Live Events: The Milano Fashion Week

  • 1. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial and Temporal Analysis of Social Media Response to Live Events: The Milano Fashion Week Marco Brambilla, Stefano Ceri, Florian Daniel, Gianmarco Donetti marco.brambilla@polimi.it marcobrambi
  • 2. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. PopeElection
  • 3. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. PopeElection Guess what
  • 4. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. PopeElection Guess what He gets elected anyway
  • 5. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Concerts?
  • 6. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Fashion?
  • 7. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Gente guarda cell in fashion
  • 8. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Gente guarda cell in fashion
  • 9. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Research Questions “Are live events still relevant? • Can online visibility be described simply by how famous is the brand? • Does physical participation still matter? • Can we predict how brands behave within events?
  • 10. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live 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 category of a brand
  • 11. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Domain Experts Official calendar Posts Posts Classified by Brand query wrangling Brands& events Data storage Data Acquisition Process
  • 12. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. The Data In fashion Prominence of Instagram Higher geolocation share
  • 13. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial Response in Time Analyze 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 events 2. The geographical positioning and timestamping of posts of social media
  • 14. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial Response in Time 1 week before the event The week of the event 1 week after the event Blue dots: social media posts Red stars: physical locations of shows [Day by day animation]
  • 15. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial Response in Time – Grid and Metrics Method: Grid over the city Density of posts in cells Measures: Gini coefficient # of alive, active and strongly active cells Average distance of posts vs. event location
  • 16. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Gini coefficient The Gini coefficient is a measure of statistical dispersion The Gini coefficient measures the inequality among values of a frequency distribution: Gini coefficient = 0 Perfect equality, where all values are the same (e.g., where every cell has the same number of posts) Gini coefficient = 1 Maximal inequality among values (e.g., where only one cell has all the social media activity, and all others have no posts geo-located inside, the Gini coefficient will be very nearly one).
  • 17. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Gini coefficient We compute the Gini coefficient: 1. Over the entire grid over the Milano area 2. Over only those cells that results alive for at least one brand in the specific frame of analysis
  • 18. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Alive, Active and Strong cells Three types of cells: ALIVE: a cell with more than 1% of the total number of posts ACTIVE: a cell with more than 10% of the total number of posts STRONG: a cell with more than 20% of the total number of posts
  • 19. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Alive, Active and Strong cells And the difference between subsequent frames (e.g. 3h -> 6h) in terms of on/off for each specific type of cell 3 hours 6 hours 2 on 1 off ALIVE cells
  • 20. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Average distance Compute the average distance of the posts from the events, in terms of cells, for each brand b, in the following way: 1 𝑛°𝑜𝑓𝑃𝑜𝑠𝑡(𝑔𝑟𝑖𝑑) 𝑐𝑒𝑙𝑙∈𝑔𝑟𝑖𝑑 𝑛°𝑜𝑓𝑃𝑜𝑠𝑡 𝑐𝑒𝑙𝑙 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑐𝑒𝑙𝑙, 𝑒𝑣𝑒𝑛𝑡. 𝑐𝑒𝑙𝑙) Using Manhattan distance for the cell grid
  • 21. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial Response in Time – Slots and Characterization Temporal granularity: 3h, 6h, 24h, whole analysis period
  • 22. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. All brands – Geo response K-Means clustering result Spatial Response in Time - Clustering Yellow, with low average distance, the most concentrated Red, with average distances slightly higher Green, with the highest results for average distance, the most dispersed Blue, a single element with highest number of alive cells K-means clustering in a PC85-dimensional space
  • 23. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Response Volume in Time Analyze the social media response to the events with respect to the axis of time Two types of signals: 1. The official calendar events 2. The volume of posts on social media Versace Number of posts: 4358 on Instagram, 389 on Twitter
  • 24. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Time Response – (pseudo) Causality Study the causality relationship between the events calendar signal and the social media signal through a series of Granger Causality tests Versace Number of posts: 4358 on Instagram, 389 on Twitter Versace Grange Causality tests result
  • 25. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Time Response – Causality Curves
  • 26. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Causality Curves Clustering K-means clustering All brands – Granger Causality tests result K-Means clustering – 4 Cluster Centroids
  • 27. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Clustered Temporal Causality All brands Granger Causality tests result All brands – Granger Causality tests result K-Means clustering – 4 Cluster Centroids Yellow, with high immediate response Red, with response peak delayed at 15 minutes Green, with delayed response peak at 45-60 minutes Blue, with mild initial response and a 3 hours lagged response peak
  • 28. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Time Response Prediction Prediction model for the cluster membership of a brand based on the Granger tests results given features related to the events and brands
  • 29. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Time Response Prediction Random 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 Predictionof the cluster membership
  • 30. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. All brands – Popularity response K-Means clustering result Brand Popularity Clustering Features: 1. # Posts on Instagram 2. # Likes on Instagram 3. # Comments on Instagram 4. # Tweets 5. # Likes on Twitter 6. # Retweets K-means clustering
  • 31. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Cluster comparison Accuracy = 44.6%
  • 32. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Cluster comparison Accuracy = 41.5%
  • 33. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Cluster comparison
  • 34. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Clustering Comparison 5 Principal Components The Confusion Matrix regarding the alignment of Time Response vs . Geo Response
  • 35. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Comparing the different dimensions A complete and well-defined brand categorization is possible only taking into account all three dimensions: Popularity Space Time Comparison Accuracy Time VS Space 44.6% Time VS Pop 41.5% Space VS Pop 38.6% Time SpacePopularity
  • 36. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Concluding… A method for comparing popularity, temporal and geographical response to brands during live events Limitations: actual presence of posting users is not granted; time and space of posts may not be aligned (e.g., delayed posts; see design scenario: 70% delayed posts) Future work: deep understanding of content for determining physical presence; data fusion with app, IoT, cell phone network
  • 37. Brambilla, Ceri, Daniel, Donetti. Spatial and Temporal Analysis of Social Media Response to Live Events. Spatial and Temporal Analysis of Social Media Response to Live Events. The Milano Fashion Week Marco Brambilla @marcobrambi marco.brambilla@polimi.it http://datascience.deib.polimi.it http://home.deib.polimi.it/marcobrambi THANKS! QUESTIONS?

Editor's Notes

  1. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  2. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  3. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  4. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  5. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  6. In particolare, abbiamo scelto come caso di studio la risposta creatasi su Twitter e Instagram a un evento dal vivo molto popolare -> Settimana della Moda di Milano (24 - 29 Feb 2016) analizzando il comportamento degli utenti che hanno agito in reazione ai suoi diversi appuntamenti. Questo è l’evento più prestigioso organizzato da Camera Nazionale della Moda Italiana, le cui sfilate della collezione donna sono il momento più atteso dal fashion system internazionale. Milano Moda Donna rappresenta il più importante punto di incontro fra prèt-à-porter e operatori del settore e regala a Milano un raffinatissimo esempio del perfetto connubio di creatività e organizzazione.
  7. Come già detto, ci siamo rivolti alle piattaforme di Twitter e Instagram, con l’obiettivo di creare due db distinti di informazioni sui contenuti condivisi, inerenti al nostro caso di studio: Con l’aiuto di alcuni esperti di dominio abbiamo definito una lista di hashtag come query da sottoporre ai due social media Abbiamo speso un po’ di tempo in tutte quelle operazioni utili a preparare, trasformare, migliorare i nostri dati, e quindi creare delle prime basi di dati orientate ai documenti, contenenti le informazioni relative ai post provenienti dalle due piattaforme Abbiamo attaccato ad ogni post la lista di brand riferiti nel testo Infine abbiamo consolidato le nostre basi di dati
  8. db di Twitter contiene quindi 106278 tweet, con una percentuale di circa il 6.5% di post geolocalizzati, che corrisponde in valore assoluto a quasi 7mila post. db di Instagram, invece, contiene poco più di 556 mila post (circa 5 volte le dimensioni del db di Twitter), con il 28% circa di media geolocalizzati (+/- 155mila post). Possiamo subito notare due fatti interessanti: Per questo specifico scenario (MFW) Instagram è stato il mezzo di comunicazione preferito utenti di Instagram risultano più propensi ad esibire la loro posizione «fisica» e quindi il coinvolgimento a un evento, (o la visita di un luogo, in generale), quasi ad indicare una prova della stessa partecipazione all’evento interessato A questo punto possiamo partire con l’esplorazione e lo studio dei nostri dati che si compone di differenti sotto-analisi -> (analizzato alcune misure proprie degli autori dei contenuti, affrontato il problema di risposta nel tempo e nello spazio ai diversi appuntamenti da calendario, e, dopo avere aggiunto un altro tipo di reazione, che definiamo di popolarità, confronto dei risultati precedenti)
  9. Successivamente, ci siamo concentrati su un’analisi della risposta ai diversi eventi della MFW, rispetto allo spazio. Analogamente a quanto visto per la risposta temporale, in questo tipo di analisi consideriamo due segnali differenti (1 per gli eventi da calendario, 1 per i volumi dei contenuti condivisi e geolocalizzati dagli utenti dei social network). Anche questo lavoro è stato fatto brand X brand, e solo per quanto riguarda Instagram. Gli eventi in esame, invece, sono tutte le sfilate di moda analizzate anche precedentemente (65). Abbiamo poi costruito una griglia su un’area di 10km*10km sopra Milano, divisa in 400 celle quadrate, di lato 500m. Con questo modello di rappresentazione ci è stato quindi possibile definire delle heatmap…
  10. Successivamente, ci siamo concentrati su un’analisi della risposta ai diversi eventi della MFW, rispetto allo spazio. Analogamente a quanto visto per la risposta temporale, in questo tipo di analisi consideriamo due segnali differenti (1 per gli eventi da calendario, 1 per i volumi dei contenuti condivisi e geolocalizzati dagli utenti dei social network). Anche questo lavoro è stato fatto brand X brand, e solo per quanto riguarda Instagram. Gli eventi in esame, invece, sono tutte le sfilate di moda analizzate anche precedentemente (65). Abbiamo poi costruito una griglia su un’area di 10km*10km sopra Milano, divisa in 400 celle quadrate, di lato 500m. Con questo modello di rappresentazione ci è stato quindi possibile definire delle heatmap…
  11. Heatmap … come la seguente, in cui andiamo ad assegnare ad ogni cella i media condivisi all’interno della cella stessa! Abbiamo quindi costruito un insieme di attributi in grado di caratterizzare ciascuna risposta spaziale, a partire da queste heatmap. Le misure adottate corrispondono a: Coefficiente di Gini, indicatore di dispersione statistica di una variabile all’interno di una popolazione Definito celle vive, attive e fortemente attive, in base alla percentuale di contenuti condivisi al loro interno dist media dei post dall’evento Inoltre, abbiamo calcolato le misure appena descritte in diverse finestre temporali: 3h + 6h +24h + GLOB. Il report in figura mostra gli attributi relativi alla risposta spaziale alla sfilata di Gucci. The Gini coefficient is a measure of statistical dispersion. It measures the inequality among values of a frequency distribution: Gini coefficient = 0 -> Perfect equality Gini coefficient = 1 -> Maximal inequality among values We compute the Gini coefficient: Over the entire grid over the Milano area Over only those cells that results alive for at least one brand in the specific frame of analysis We have defined three types of cells: ALIVE: a cell with more than 1% of the total number of posts ACTIVE: a cell with more than 10% of the total number of posts STRONG: a cell with more than 20% of the total number of posts We compute these measures for all the pre-defined frames and we also compute the differences between subsequent frames in terms of on/off for each specific type of cell. Compute the average distance of the posts from the events, in terms of cells, using Manhattan distance.
  12. Heatmap … come la seguente, in cui andiamo ad assegnare ad ogni cella i media condivisi all’interno della cella stessa! Abbiamo quindi costruito un insieme di attributi in grado di caratterizzare ciascuna risposta spaziale, a partire da queste heatmap. Le misure adottate corrispondono a: Coefficiente di Gini, indicatore di dispersione statistica di una variabile all’interno di una popolazione Definito celle vive, attive e fortemente attive, in base alla percentuale di contenuti condivisi al loro interno dist media dei post dall’evento Inoltre, abbiamo calcolato le misure appena descritte in diverse finestre temporali: 3h + 6h +24h + GLOB. Il report in figura mostra gli attributi relativi alla risposta spaziale alla sfilata di Gucci. The Gini coefficient is a measure of statistical dispersion. It measures the inequality among values of a frequency distribution: Gini coefficient = 0 -> Perfect equality Gini coefficient = 1 -> Maximal inequality among values We compute the Gini coefficient: Over the entire grid over the Milano area Over only those cells that results alive for at least one brand in the specific frame of analysis We have defined three types of cells: ALIVE: a cell with more than 1% of the total number of posts ACTIVE: a cell with more than 10% of the total number of posts STRONG: a cell with more than 20% of the total number of posts We compute these measures for all the pre-defined frames and we also compute the differences between subsequent frames in terms of on/off for each specific type of cell. Compute the average distance of the posts from the events, in terms of cells, using Manhattan distance.
  13. Anche in questo caso ci siamo posti l’obiettivo di raggruppare i brand per risposta spaziale simile. Il risultato dell’algoritmo di k-means con 4 gruppi sfruttando solamente quegli attributi che ci permettessero di descrivere i dati con l’85% della varianza totale è il seguente: abbiamo il gruppo giallo, quello rosso, quello verde, e quello blu, con un singolo elemento al suo interno. E in quest’ordine andiamo dal gruppo di elementi con risposta più concentrata, a quello con risposta più dispersa
  14. Innanzitutto, ci siamo concentrati su un’analisi della risposta ai diversi eventi della MFW, rispetto al tempo. Definendo meglio il problema, in questo tipo di analisi consideriamo due segnali temporali (1 per gli eventi da calendario, 1 per i volumi dei contenuti condivisi nei social media). Questo lavoro è stato fatto brand X brand, selezionando dunque un brand alla volta e facendo riferimento: agli eventi relativi al brand specifico ai post che fanno riferimento allo stesso brand Gli eventi in esame sono quasi totalmente delle sfilate di moda (femminile), della durata di 15-30 minuti, con partecipazione ad invito, quindi chiuse al pubblico. Ci siamo rivolti alla sola piattaforma di Instagram, dato il più elevato numero di contenuti condivisi
  15. Abbiamo studiato una sorta di causalità predittiva tra i due segnali differenti, con l’aiuto dei Test di Causalità di G. Questi test vanno ad analizzare la causalità che un segnale temporale esercita su un altro. In breve, una serie temporale si dice che è causa (nel senso di G) di una seconda serie temporale, se può essere dimostrato, mediante una serie di test F su valori ritardati della prima serie, che tali valori della prima serie (causa) forniscono informazioni statisticamente significative sui futuri valori della seconda serie (effetto). Nel grafico viene riportato il valore della statistica F all’aumentare del ritardo applicato alla serie causa. Questo tipo di test è costruito in modo tale che la statistica F tenda ad essere grande quando l’ipotesi di non causalità è da rifiutare, ossia quando viene accettato un rapporto causale. Quindi, la posizione del picco va ad indicare il ritardo con cui si può stabilire una netta e massima causalità tra il segnale dell’evento e il segnale di risposta social.
  16. Abbiamo quindi effettuato i test di Causalità di Granger per 73 brand (e eventi correlati) e nel primo grafico possiamo osservare tutte le curve della statistica F ottenute, normalizzate in modo da avere il picco a 1. In questo modo stiamo considerando solo la forma e la posizione relativa del picco di tali curve, perdendo le informazioni legate alle quantità di contenuti condivisi. A questo punto, abbiamo cercato di eseguire un raggruppamento dei brand in base a tali risultati, con lo scopo di individuare comportamenti simili tra i diversi tipi di risposta temporale. Abbiamo applicato l’algoritmo di k-means in uno spazio L-dimensionale, dove L indica il numero di ritardi che abbiamo precedentemente studiato Risultati -> definire 4 cluster, colori, tipo di risposta
  17. Abbiamo quindi effettuato i test di Causalità di Granger per 73 brand (e eventi correlati) e nel primo grafico possiamo osservare tutte le curve della statistica F ottenute, normalizzate in modo da avere il picco a 1. In questo modo stiamo considerando solo la forma e la posizione relativa del picco di tali curve, perdendo le informazioni legate alle quantità di contenuti condivisi. A questo punto, abbiamo cercato di eseguire un raggruppamento dei brand in base a tali risultati, con lo scopo di individuare comportamenti simili tra i diversi tipi di risposta temporale. Abbiamo applicato l’algoritmo di k-means in uno spazio L-dimensionale, dove L indica il numero di ritardi che abbiamo precedentemente studiato Risultati -> definire 4 cluster, colori, tipo di risposta
  18. Abbiamo quindi effettuato i test di Causalità di Granger per 73 brand (e eventi correlati) e nel primo grafico possiamo osservare tutte le curve della statistica F ottenute, normalizzate in modo da avere il picco a 1. In questo modo stiamo considerando solo la forma e la posizione relativa del picco di tali curve, perdendo le informazioni legate alle quantità di contenuti condivisi. A questo punto, abbiamo cercato di eseguire un raggruppamento dei brand in base a tali risultati, con lo scopo di individuare comportamenti simili tra i diversi tipi di risposta temporale. Abbiamo applicato l’algoritmo di k-means in uno spazio L-dimensionale, dove L indica il numero di ritardi che abbiamo precedentemente studiato Risultati -> definire 4 cluster, colori, tipo di risposta
  19. L’ultimo problema che abbiamo affrontato per quanto concerne la risposta temporale, è stato quello di ideare dei modelli predittivi in grado di classificare il tipo di risposta temporale per ciascuna coppia brand-evento, con l’obiettivo di stabilire l’appartenenza di questi elementi a uno dei gruppi appena descritti. Con questo scopo abbiamo provato ad utilizzare diverse tecniche, [RegLog, MaccVettSupp, AlbDec, ForCas] «addestrando» tali modelli con le scarse informazioni relative agli eventi, e adottando un approccio di LOO-CV. Abbiamo confrontato le performance dei diversi modelli: tra di loro con le performance di classificatori banali (generatori casuali o stratificati) I risultati, in termini di stima di probabilità + accuratezza di classificazione, sono stati soddisfacenti per tutti i modelli costruiti e, in particolare…
  20. … la nostra ForCas ha ottenuto un’accuratezza di circa il 62% con 45 classificazioni correte e 28 sbagliate. (Dai grafici, si nota come, in generale, il comportamento della RF è abbastanza soddisfacente)
  21. … di popolarità! Infatti, abbiamo aggiunto alla nostra analisi un ulteriore tipo di reazione sociale, che definiamo risposta di popolarità. In questo veloce studio, abbiamo caratterizzato ciascun brand con gli attributi riportati [numero di contenuti sui due social media, numero di like, commenti e retweet], e applicato nuovamente l’algoritmo di k-means per raggruppare i brand in base alla popolarità che i post riferiti ai singoli brand hanno riscosso. Il risultato è una divisione dei brand in gruppi, dai meno popolari, in rosso, ai più popolari, in blu.
  22. Abbiamo quindi accostato le differenti analisi provenienti dai 3 diversi «universi» e i risultati sono del tipo della slide stiamo osservando. Nel caso del confronto tra risposta temporale e risposta spaziale, il miglior accostamento in termini di accuratezza (punteggio 44.6%) va a far combaciare le diverse classi come indicato nella slide. Una risposta immediata con una risposta geografica non troppo diffusa Una risposta ritardata di 15min con una risposta molto diffusa nello spazio Una risposta con picco dopo 45 minuti con la risposta spaziale più concentrata Una risposta temporale con due picchi, uno immediato e uno molto ritardato, alla risposta geografica il più diffusa possibile
  23. Abbiamo effettuato questo tipo di accostamento per tutte e 3 le coppie possibili, e i risultati, in termini di accuratezza, ci indicano come per caratterizzare, definire e valutare l’impatto sui social media dei diversi brand e dei loro eventi, è stato utile e necessario affrontare tutte e 3 le differenti analisi proposte. Infatti, nessun’accostamento ci rivela una netta correlazione tra diversi universi di risposta, a prova del fatto che le informazioni su un certo tipo di reazione riescono a darci poche indicazioni sulle altre dimensioni prese in considerazione.