336 research outputs found
When Do Luxury Cars Hit the Road? Findings by A Big Data Approach
In this paper, we focus on studying the appearing time of different kinds of
cars on the road. This information will enable us to infer the life style of
the car owners. The results can further be used to guide marketing towards car
owners. Conventionally, this kind of study is carried out by sending out
questionnaires, which is limited in scale and diversity. To solve this problem,
we propose a fully automatic method to carry out this study. Our study is based
on publicly available surveillance camera data. To make the results reliable,
we only use the high resolution cameras (i.e. resolution greater than ). Images from the public cameras are downloaded every minute. After
obtaining 50,000 images, we apply faster R-CNN (region-based convoluntional
neural network) to detect the cars in the downloaded images and a fine-tuned
VGG16 model is used to recognize the car makes. Based on the recognition
results, we present a data-driven analysis on the relationship between car
makes and their appearing times, with implications on lifestyles
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
In this paper, we study follower demographics of Donald Trump and Hillary
Clinton, the two leading candidates in the 2016 U.S. presidential race. We
build a unique dataset US2016, which includes the number of followers for each
candidate from September 17, 2015 to December 22, 2015. US2016 also includes
the geographical location of these followers, the number of their own followers
and, very importantly, the profile image of each follower. We use individuals'
number of followers and profile images to analyze four dimensions of follower
demographics: social status, gender, race and age. Our study shows that in
terms of social influence, the Trumpists are more polarized than the
Clintonists: they tend to have either a lot of influence or little influence.
We also find that compared with the Clintonists, the Trumpists are more likely
to be either very young or very old. Our study finds no gender affinity effect
for Clinton in the Twitter sphere, but we do find that the Clintonists are more
racially diverse.Comment: 4 pages, to appear in the 10th International AAAI Conference on Web
and Social Medi
Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities
With the rapid development of economy in China over the past decade, air
pollution has become an increasingly serious problem in major cities and caused
grave public health concerns in China. Recently, a number of studies have dealt
with air quality and air pollution. Among them, some attempt to predict and
monitor the air quality from different sources of information, ranging from
deployed physical sensors to social media. These methods are either too
expensive or unreliable, prompting us to search for a novel and effective way
to sense the air quality. In this study, we propose to employ the state of the
art in computer vision techniques to analyze photos that can be easily acquired
from online social media. Next, we establish the correlation between the haze
level computed directly from photos with the official PM 2.5 record of the
taken city at the taken time. Our experiments based on both synthetic and real
photos have shown the promise of this image-based approach to estimating and
monitoring air pollution.Comment: ICIMCS '1
Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'
We propose a framework to measure, evaluate, and rank campaign effectiveness
in the ongoing 2016 U.S. presidential election. Using Twitter data collected
from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the
candidates and second, using negative binomial regression and exploiting the
variations in 'likes,' we evaluate the effectiveness of these tactics. Thirdly,
we rank the candidates' campaign tactics by calculating the conditional
expectation of their generated 'likes.'
We show that while Ted Cruz and Marco Rubio put much weight on President
Obama, this tactic is not being well received by their supporters. We
demonstrate that Hillary Clinton's tactic of linking herself to President Obama
resonates well with her supporters but the same is not true for Bernie Sanders.
In addition, we show that Donald Trump is a major topic for all the other
candidates and that the women issue is equally emphasized in Sanders' campaign
as in Clinton's.
Finally, we suggest two ways that politicians can use the feedback mechanism
in social media to improve their campaign: (1) use feedback from social media
to improve campaign tactics within social media; (2) prototype policies and
test the public response from the social media.Comment: ICWSM 2017 - News and Public Opinion Worksho
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