42 research outputs found
Analysis of Home Location Estimation with Iteration on Twitter Following Relationship
User's home locations are used by numerous social media applications, such as
social media analysis. However, since the user's home location is not generally
open to the public, many researchers have been attempting to develop a more
accurate home location estimation. A social network that expresses
relationships between users is used to estimate the users' home locations. The
network-based home location estimation method with iteration, which propagates
the estimated locations, is used to estimate more users' home locations. In
this study, we analyze the function of network-based home location estimation
with iteration while using the social network based on following relationships
on Twitter. The results indicate that the function that selects the most
frequent location among the friends' location has the best accuracy. Our
analysis also shows that the 88% of users, who are in the social network based
on following relationships, has at least one correct home location within
one-hop (friends and friends of friends). According to this characteristic of
the social network, we indicate that twice is sufficient for iteration.Comment: The 2016 International Conference on Advanced Informatics: Concepts,
Theory and Application (ICAICTA2016
When Do Users Change Their Profile Information on Twitter?
We can see profile information such as name, description and location in
order to know the user on social media. However, this profile information is
not always fixed. If there is a change in the user's life, the profile
information will be changed. In this study, we focus on user's profile
information changes and analyze the timing and reasons for these changes on
Twitter. The results indicate that the peak of profile information change
occurs in April among Japanese users, but there was no such trend observed for
English users throughout the year. Our analysis also shows that English users
most frequently change their names on their birthdays, while Japanese users
change their names as their Twitter engagement and activities decrease over
time.Comment: IEEE BigData 2017 Workshop : The 2nd International Workshop on
Application of Big Data for Computational Social Science (accepted
Home Location Estimation Using Weather Observation Data
We can extract useful information from social media data by adding the user's
home location. However, since the user's home location is generally not
publicly available, many researchers have been attempting to develop a more
accurate home location estimation. In this study, we propose a method to
estimate a Twitter user's home location by using weather observation data from
AMeDAS. In our method, we first estimate the weather of the area posted by an
estimation target user by using the tweet, Next, we check out the estimated
weather against weather observation data, and narrow down the area posted by
the user. Finally, the user's home location is estimated as which areas the
user frequently posts from. In our experiments, the results indicate that our
method functions effectively and also demonstrate that accuracy improves under
certain conditions.Comment: The 2017 International Conference On Advanced Informatics: Concepts,
Theory And Application (ICAICTA2017
Response Collector: A Video Learning System for Flipped Classrooms
The flipped classroom has become famous as an effective educational method
that flips the purpose of classroom study and homework. In this paper, we
propose a video learning system for flipped classrooms, called Response
Collector, which enables students to record their responses to preparation
videos. Our system provides response visualization for teachers and students to
understand what they have acquired and questioned. We performed a practical
user study of our system in a flipped classroom setup. The results show that
students preferred to use the proposed method as the inputting method, rather
than naive methods. Moreover, sharing responses among students was helpful for
resolving individual students' questions, and students were satisfied with the
use of our system.Comment: The 2018 International Conference On Advanced Informatics: Concepts,
Theory And Application (ICAICTA2018
Computing Information Quantity as Similarity Measure for Music Classification Task
This paper proposes a novel method that can replace compression-based
dissimilarity measure (CDM) in composer estimation task. The main features of
the proposed method are clarity and scalability. First, since the proposed
method is formalized by the information quantity, reproduction of the result is
easier compared with the CDM method, where the result depends on a particular
compression program. Second, the proposed method has a lower computational
complexity in terms of the number of learning data compared with the CDM
method. The number of correct results was compared with that of the CDM for the
composer estimation task of five composers of 75 piano musical scores. The
proposed method performed better than the CDM method that uses the file size
compressed by a particular program.Comment: The 2017 International Conference On Advanced Informatics: Concepts,
Theory And Application (ICAICTA2017
Analysis of the Influence of Internet TV Station on Wikipedia Page Views
We aim to investigate the influence of television on the web; if the
influence is strong, a viral effect may be expected. In this paper, we focus on
the Internet TV station and on Wikipedia use as exploratory behavior on the
web. We analyzed the influence of Internet TV station on Wikipedia page views.
Our aim is to clarify the characteristics of page views as related to Internet
TV station in order to index outward impact and develop a prediction model. The
results indicate that there is a correlation between TV viewership and page
views. Moreover we find that the time lag between TV and web gradually reduce
as broadcasts begin after 9:00; after 23:00, page views tend to be maximized
during the broadcast itself. We also differentiate between page views on PC and
on mobile and find that PC pages tend to be accessed more during the daytime.
In addition, we consider the number of broadcasts per program, and observe that
viewership tends to stabilize as the number of broadcasts increases but that
page views tend to decrease.Comment: The 3rd International Workshop on Application of Big Data for
Computational Social Science (ABCSS2018