Why we twitter: understanding microblogging usage and communities

Lecture Notes in Computer Science, 2009

Microblogging is a new form of communication in which users describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. We present our observations of the microblogging phenomena by studying the topological and geographical properties of the social network in Twitter, one of the most popular microblogging systems. We find that people use microblogging primarily to talk about their daily activities and to seek or share information. We present a taxonomy characterizing the ...

Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis - WebKDD/SNA-KDD '07, 2007

Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.

Proceedings of the 19th …, 2010

Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing.

Proceedings of Hybrid City Conference 2013, 2013

This paper examines the community formed by the Twitter users that used a city-level hashtag. In particular, we provide a network perspective of the city of Athens, Greece, as demonstrated by the analysis and visualization of the relevant Twitter hashtag data, in order to present both an overview and deeper insights at the microblogging practices of this geographic local network. Further analysis suggests that the Twitter community defined by the members of the network shows strong signs of a real-life community.

Detailed knowledge regarding the whereabouts of people and their social activities in urban areas with high spatial and temporal resolution is still widely unexplored. Thus, the spatiotemporal analysis of Location Based Social Networks (LBSN) has great potential regarding the ability to sense spatial processes and to gain knowledge about urban dynamics, especially with respect to collective human mobility behavior. The objective of this paper is to explore the semantic association between georeferenced tweets and their respective spatiotemporal whereabouts. We apply a semantic topic model classification and spatial autocorrelation analysis to detect tweets indicating specific human social activities. We correlated observed tweet patterns with official census data for the case study of London in order to underline the significance and reliability of Twitter data. Our empirical results of semantic and spatiotemporal clustered tweets show an overall strong positive correlation in comparison with workplace population census data, being a good indicator and representative proxy for analyzing workplace-based activities.

This paper compares the social properties of Twitter users’ networks with the spatial proximity of the networks. Using a comprehensive analysis of network density and network transitivity we found that the density of networks and the spatial clustering depends on the size of the network; smaller networks are more socially clustered and extend a smaller physical distance and larger networks are physically more dispersed with less social clustering. Additionally, Twitter networks are more effective at transmitting information at the local level. For example, local triadic connections are more than twice as likely to be transitive than those extending more than 500 km. This implies that not only is distance important to the communities developed in online social networks, but scale is extremely pertinent to the nature of these networks. Even as technologies such as Twitter enable a larger volume of interaction between spaces, these interactions do not invent completely new social and spatial patterns, but instead replicate existing arrangements.

… Scientist, Special issue …, 2011

International Journal of the Physical Sciences, 2012

Social media is the baby born out of the confluence of digital technology and human beings' desire to collaborate. Past researches in social media networks have mostly concentrated on investigation of large networks, which do not fully capture the micro-level dynamics of the network. In this study, an indepth topological analysis of a small network (n=200) formed on Twitter during a 24 h period was carried out. The results showed that the network had both small-world and scale-free characteristics. Geo-spatiality revealed more interest by users in regions where the subject of tweets had its stake. The most influential nodes were those whose tweets got re-tweeted the most. Temporal analysis showed faster formation of network when there was a tweet of interest. Traditional news media had a powerful hold on the tweets being made by users. Communities formed around tweets of a certain theme and there was a common theme that kept the entire network together.

Proceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data - GeoRich'14, 2007

Microblogs allow users to publish geo-tagged posts-short textual messages assigned to a geographic location. Users send posts from places they visit and discuss an idiosyncratic mixture of personal and general topics. Thus, it is reasonable to assume that the locations and the textual content of posts will be unique and will identify the posting user, to some extent. This raises the question whether there is a correlation between the locations of posts and their content. Are users who are similar from the geospatial perspective (i.e., who send messages from nearby locations) also similar from the textual perspective (i.e., send messages with similar textual content)? Do posts with similar content have a spatial distribution similar to that of any random set of posts? We present a study that focuses on these questions. We provide statistical tests to examine the correlation between textual content and geospatial locations in tweets. We show that although there is some correlation between locations and textual content, they provide different similarity measures, and combining these two properties for identification of users by their posts outperforms methods that merely use locations or only use the textual content, for identification.

Geo-spatial Information Science, 2014

The penetration and use of social media services differs from city to city. This paper is aimed to provide a comparison of the use of Twitter between different cities of the world. We present a temporal analysis of activity on Twitter in 15 cities. Our study consists of two parts: First, we created temporal graphs of the activity in the 15 cities, through which hours of high and low activity could be identified. Second, we created heat map visualizations of the Twitter activities during the period of 19 September 2012-25 September 2013. The heat map visualizations make the periods of intense and sparse activity apparent and provide a snapshot of the activity during the whole year.