Shoko Wakamiya

Discovery of unusual regional social activities using geo-tagged microblogs

The advent of microblogging services represented by Twitter evidently stirred a popular trend of personal update sharing from all over the world. Furthermore, the recent mobile device and wireless network technologies are greatly expanding the connectivity between people over the social networking sites. Regarding the shared buzzes over the sites as a crowd-sourced database reflecting a various kind of real-world events, we are able to conduct a variety of social analytics using the crowd power in much easier ways. In this paper, we propose a geo-social event detection method by finding out unusually crowded places based on the conception of social networking sites as a social event detector. In order to detect unusual statuses of a region, we previously construct geographical regularities deduced from geo-tagged microblogs. Especially, we utilize a large number of geo-tagged Twitter messages which are collected by means of our own tweets acquisition method in terms of geographic relevancy. By comparing to those regularities, we decide if there are any unusual events happening in monitoring geographical areas. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities which are computed from a large number of real geo-tagged tweet dataset around Japan.

Urban area characterization based on crowd behavioral lifelogs over Twitter

Recent location-based social networking sites are attractively providing us with a novel capability of monitoring massive crowd lifelogs in the real-world space. In particular, they make it easier to collect publicly shared crowd lifelogs in a large scale of geographic area reflecting the crowd’s daily lives and even more characterizing urban space through what they have in minds and how they behave in the space. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing crowd lifelogs in urban area over the social networking sites. In order to collect crowd behavioral data, we exploit the most famous microblogging site, Twitter, where a great deal of geo-tagged micro lifelogs emitted by massive crowds can be easily acquired. We first present a model to deal with crowds’ behavioral logs on the social network sites as a representing feature of urban space’s characteristics, which will be used to conduct crowd-based urban characterization. Based on this crowd behavioral feature, we will extract significant crowd behavioral patterns in a period of time. In the experiment, we conducted the urban characterization by extracting the crowd behavioral patterns and examined the relation between the regions of common crowd activity patterns and the major categories of local facilities.

Urban area characterization based on semantics of crowd activities in Twitter

It is essential to characterize geographic regions in order to make various geographic decisions. These regions can be characterized from various perspectives such as the physical appearance of a town. In this paper, as a novel approach to characterize geographic regions, we focus on the daily lifestyle patterns of crowds via location-based social networking sites in urban areas. For this purpose, we propose a novel method to characterize urban areas using Twitter, the most representative microblogging site. In order to grasp images of a city by social network based crowds, we define the geographic regularity of the region using daily crowd activity patterns; for instance, the number of tweets, through the number of users, and the movement of the crowds. We also analyze the changing patterns of geographic regularity with time and categorize clustered urban types by tracking common patterns among the regions. Finally, we present examples of several urban types through the observation of experimentally extracted patterns of crowd behavior in actual urban areas.

Crowd-sourced urban life monitoring: urban area characterization based crowd behavioral patterns from Twitter

Location-based social network sites are recently attracting a great deal of attention by combing Web-based social network and the real-world location tagging in an integrated way, where people can publish their life logs about their real-world activities and share them with the public often looking for location-based information. Obviously, in terms of technological and social advance such as location sensing smartphones, experiences and thoughts by the unexpectedly growing number of the mobile users in urban area are conveniently being shared significantly impacting our ways of life experience sharing. In such context, we are able to monitor crowds experiences through the location-based social network by collecting and analyzing crowds numerous micro life logs to support a variety of decision makings. In this paper, we attempt to look into the crowds urban lifestyles, which are characterizing urban areas, particularly utilizing Twitter. We provide a model to construct systems for a large-scale urban analytics with the location-based social network. We also describe our practical approach to describe urban characteristics represented by crowds temporal behavioral patterns. In the experiment, we show an urban characterization by way of crowds behavioral patterns, which are derived from temporal patterns of crowd behavior indirectly speculated from a massive number of collected Twitter messages. Finally, we discuss the importance of this kind of challenge amid the pervasive social network environment and some critical issues to be considered for the wide spectrum of sociological studies requiring technology-driven crowd life monitoring.

Portraying Collective Spatial Attention in Twitter

Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.

Social-Urban Neighborhood Search Based on Crowd Footprints Network

Neighborhood is generally a geographically localized community often with face-to-face social interactions. However, modern cities and the widespread social networks have been drastically changing the concept of neighborhood, much beyond spatial constraint. Specifically, due to the complicated urban structures with entangled transportation network and the resulting spatio-temporally extended crowd activities, it is a non-trivial task to examine neighborhood areas from a location of interest. As a promising approach to investigate such a social-urban structure, we propose a social-urban neighborhood search which aims at identifying neighborhood areas from a specific location particularly considering social interactions between urban areas. We especially examine crowd movings through location-based social networks as an important indicator for measuring social interactions. We also introduce a data structure for aggregation of crowd movings as a simplified graph, with which we can easily analyze crowd movements in a large scale urban area. In the experiment, we will look into neighborhoods for several urban areas of our interests in terms of social interactions significantly focusing on how they are distorted from general localized vicinity.

Crowd-sourced cartography: measuring socio-cognitive distance for urban areas based on crowd's movement

On behalf of the rapid urbanization, urban areas are gradually becoming a sophisticated space where we often need to know ever evolving features to take the most of the space. Therefore, keeping up with the dynamic change of urban space would be necessary, while it usually requires lots of efforts to understand newly visiting and daily changing living spaces. In order to explore and exploit the urban complexity from crowd-sourced lifelogs, we focus on location-based social network sites. In fact, due to the proliferation of location-based social networks, we can easily acquire massive crowd-sourced lifelogs interestingly indicating their experiences in the real space. In particular, we can conduct various novel urban analytics by monitoring crowds experiences in an unprecedented way. In this paper, we particularly attempt to exploit crowd-sourced location-based lifelogs for generating a socio-cognitive map, whose purpose is to deliver much simplified and intuitive perspective of urban space. For the purpose, we measure socio-cognitive distance among urban clusters based on human mobility to represent accessibility of urban areas based on crowds movement. Finally, we generate a socio-cognitive map reflecting the proposed socio-cognitive distances which have computed with massive geo-tagged tweets from Twitter.

Lets not stare at smartphones while walking: memorable route recommendation by detecting effective landmarks

Navigation in unfamiliar cities often requires frequent map checking, which is troublesome for wayfinders. We propose a novel approach for improving real-world navigation by generating short, memorable and intuitive routes. To do so we detect useful landmarks for effective route navigation. This is done by exploiting not only geographic data but also crowd footprints in Social Network Services (SNS) and Location Based Social Networks (LBSN). Specifically, we detect point, area, and line landmarks by using three indicators to measure landmarks utility: visit popularity, direct visibility, and indirect visibility. We then construct an effective route graph based on the extracted landmarks, which facilitates optimal path search. In the experiments, we show that landmark-based routes out-perform the ones created by baseline from the perspectives of the lap time and the number of references necessary to check self-positions for adjusting route directions.

Measuring Crowd Mood in City Space Through Twitter

In this paper, we measure crowd mood and investigate its spatio-temporal distributions in a large-scale urban area through Twitter. In order to exploit tweets as a source to survey crowd mind, we propose two measurements which extract and categorize semantic terms from texts of tweets based on a dictionary of emotional terms. In particular, we focus on how to aggregate crowd mood quantitatively and qualitatively. n the experiment, the proposed methods are applied to a large tweets dataset collected for an urban area in Japan. From the daily tweets, we were able to observe interesting temporal changes in crowd’s positive and negative moods and also identified major downtown areas where crowd’s emotional tweets are intensively found. In this preliminary work, we confirme the diversity of urban areas in terms of crowd moods which are observed from the crowd-sourced lifelogs on Twitter.

Scene extraction system for video clips using attached comment interval and pointing region

A method was developed to enable users of video sharing websites to easily retrieve video scenes relevant to their interests. The system analyzes both text and non-text aspects of a user’s comment and then retrieves and displays relevant scenes along with attached comments. The text analysis works in tandem with non-text features, namely, the selected area and temporal duration associated with user comments. In this way, our system supports a better-organized retrieval of scenes that have been commented on with a higher degree of relevancy than conventional methods, such as using matching keywords. We describe our method and the relation between the scenes and discuss a prototype system.