A Large-scale Analysis of Regional
Tendency of Twitter Photos Using
Only Image Features
Tetsuya Nagano, Takumi Ege, Wataru Shimoda and Keiji
Yanai Department of Informatics, The University of ElectroCommunications, Tokyoⓒ 2017 UEC Tokyo.

Introduction• Many people to post photos on open SNSs suchas Twitter and Instagramⓒ 2017 UEC Tokyo.

Introduction• Flickr– Textual information attached to photos for photosharing• Twitter, Instagram– Do not tend to represent the contents of photos
directly– Not for search but for explaining additionalinformationⓒ 2017 UEC Tokyo.

Introduction• Analyze the differences on idea of the privacy
issue at SNSs depends on culture and historyⓒ 2017 UEC Tokyo.

Related Works• Typical works on Twitter photo,– Event detection– Using both text analysis and image analysis• Our work
– We analyze a million-scale of Twitter photos
without using any textual information– We aim to detect the differences of regionaltendency of posted photos to Twitterⓒ 2017 UEC Tokyo.

Overview of the Proposed Method• (1) Gather a million-scale of geotagged photos
from the Twitter stream• (2) Extract image features• (3) Carry out clustering of them• (4) Classify only large clusters• (5) Compare the ratios of five categories
between eight regions over the worldⓒ 2017 UEC Tokyo.

Detail of the Method• We are continuously gathering geotagged
photo tweets from the Twitter stream• Two million geotagged Twitter photos which
we had gathered for half years in 2016.
Messages
LocationImageⓒ 2017 UEC Tokyo.

Location
ImageImage Classification• AlexNet (CaffeNet)– Extract CNN features of 1000 images per one minutes• FC6 layer of CaffeNet is 4096
• Principal Component Analysis (PCA)– 4096-d features into 128-d features
Input
3x256x256

Layer1Conv
96x24x2
4

Layer2Conv
256x12x12

Layer3Conv
512x12x12

Layer5
Conv
1024x6x6

Layer6full
3072x1x1

Layer7full
4096x1x1ⓒ 2017 UEC Tokyo.

Layer4
Conv
1024x12x
12
Layer8full
1000x1x1

Output1000x1x1

Clustering• K-means clustering.
– k = 100

• A small pre-liminary experiments with 1000
Twitter photos– To examine the difference on clustering results
between the cases with and without PCA-based
feature compressionⓒ 2017 UEC Tokyo.

Clusteling results (4096-d)ⓒ 2017 UEC Tokyo.

Clusteling results (128-d)ⓒ 2017 UEC Tokyo.

5 Experiments• Dataset– Collected from January to June in 2016
– 2,161,000 geotagged Twitter images• Feature– CNN(128-d compressed by PCA)

• K-means– K-means with one-tenth images• Assigned rest of images into the nearest clusters– K=100ⓒ 2017 UEC Tokyo.

5.3 How to Analyze• East Asia, North America, South America,
Europe, Africa, Middle East, South Asia andSouth-East Asia, Oceaniaⓒ 2017 UEC Tokyo.

5.3 How to Analyze• pre-selected photo genres.
– “people”– “building”– “document”– “scene”– “food”ⓒ 2017 UEC Tokyo.

Analysis of Regional Tendency ofPhoto Genres
1st Europe
183,250枚
3rd North America
124,141枚

2rd South east Asia
145,088枚ⓒ 2017 UEC Tokyo.

ⓒ 2017 UEC Tokyo.

• East Asia
– No people photos– Many building and food photos– The total ratio of building and food photos weremore than 70%
PeopleBuilding
Document
LandscapeFoodⓒ 2017 UEC Tokyo.

• North America– The ratio of people and building were high morethan 60%.
PeopleBuilding
Document
Landscape
Foodⓒ 2017 UEC Tokyo.

• South America– People photos are the most popular genre (67%)
PeopleBuilding
Document
LandscapeFoodⓒ 2017 UEC Tokyo.

Analysis of Regional Tendency of
Photo Genres• Europe– The number of posted photos was the most large– The genres were well balanced.

• Africa– Almost no building, scene and food photos wereposted– People photos occupied 70%.

ⓒ 2017 UEC Tokyo.

• Middle East
– Although the number of posts were fewer thanEurope, all the five genres were balanced as well
PeopleBuilding
Document
LandscapeFoodⓒ 2017 UEC Tokyo.

Analysis of Regional Tendency ofPhoto Genres• South Asia

– More than half of the photos were document photos.
ss– This tendency was notobserved in other regions• SouthEast Asia
– People photos are the most and in addition foodphotos was the second mostⓒ 2017 UEC Tokyo.

Sourth America：Peopleⓒ 2017 UEC Tokyo.

Africa：Peopleⓒ 2017 UEC Tokyo.

East Asia：Foodⓒ 2017 UEC Tokyo.

Sourth East Asia：Foodⓒ 2017 UEC Tokyo.

Discussion• Tendency– East Asia and East-South Asia,• Food photos are relatively high– South America, South Asia and East-South Asia
• people photos are exceptionally high.

– Europe and MiddleEast• well balanced.

• East Asia enjoy posting food photos• South America, South Asia and EastSouth Asia liketo post people photos without caring privacy issue.
ⓒ 2017 UEC Tokyo.

Conclusions• Analyzed the differences of the tendency of thephoto genres of posted photos.
– Gathered geotagged Twitter photos– Extracted CNN features– Carried out clustering• Future works– making typical genres more fine-grained• “people photo” -> “selfy” and“group photo”ⓒ 2017 UEC Tokyo.

ⓒ 2017 UEC Tokyo.