1,492 research outputs found
Traveling Trends: Social Butterflies or Frequent Fliers?
Trending topics are the online conversations that grab collective attention
on social media. They are continually changing and often reflect exogenous
events that happen in the real world. Trends are localized in space and time as
they are driven by activity in specific geographic areas that act as sources of
traffic and information flow. Taken independently, trends and geography have
been discussed in recent literature on online social media; although, so far,
little has been done to characterize the relation between trends and geography.
Here we investigate more than eleven thousand topics that trended on Twitter in
63 main US locations during a period of 50 days in 2013. This data allows us to
study the origins and pathways of trends, how they compete for popularity at
the local level to emerge as winners at the country level, and what dynamics
underlie their production and consumption in different geographic areas. We
identify two main classes of trending topics: those that surface locally,
coinciding with three different geographic clusters (East coast, Midwest and
Southwest); and those that emerge globally from several metropolitan areas,
coinciding with the major air traffic hubs of the country. These hubs act as
trendsetters, generating topics that eventually trend at the country level, and
driving the conversation across the country. This poses an intriguing
conjecture, drawing a parallel between the spread of information and diseases:
Do trends travel faster by airplane than over the Internet?Comment: Proceedings of the first ACM conference on Online social networks,
pp. 213-222, 201
#Santiago is not #Chile, or is it? A Model to Normalize Social Media Impact
Online social networks are known to be demographically biased. Currently
there are questions about what degree of representativity of the physical
population they have, and how population biases impact user-generated content.
In this paper we focus on centralism, a problem affecting Chile. Assuming that
local differences exist in a country, in terms of vocabulary, we built a
methodology based on the vector space model to find distinctive content from
different locations, and use it to create classifiers to predict whether the
content of a micro-post is related to a particular location, having in mind a
geographically diverse selection of micro-posts. We evaluate them in a case
study where we analyze the virtual population of Chile that participated in the
Twitter social network during an event of national relevance: the municipal
(local governments) elections held in 2012. We observe that the participating
virtual population is spatially representative of the physical population,
implying that there is centralism in Twitter. Our classifiers out-perform a non
geographically-diverse baseline at the regional level, and have the same
accuracy at a provincial level. However, our approach makes assumptions that
need to be tested in multi-thematic and more general datasets. We leave this
for future work.Comment: Accepted in ChileCHI 2013, I Chilean Conference on Human-Computer
Interactio
Crowdsourcing Dialect Characterization through Twitter
We perform a large-scale analysis of language diatopic variation using
geotagged microblogging datasets. By collecting all Twitter messages written in
Spanish over more than two years, we build a corpus from which a carefully
selected list of concepts allows us to characterize Spanish varieties on a
global scale. A cluster analysis proves the existence of well defined
macroregions sharing common lexical properties. Remarkably enough, we find that
Spanish language is split into two superdialects, namely, an urban speech used
across major American and Spanish citites and a diverse form that encompasses
rural areas and small towns. The latter can be further clustered into smaller
varieties with a stronger regional character.Comment: 10 pages, 5 figure
White, Man, and Highly Followed: Gender and Race Inequalities in Twitter
Social media is considered a democratic space in which people connect and
interact with each other regardless of their gender, race, or any other
demographic factor. Despite numerous efforts that explore demographic factors
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this paper, we attempt to identify
gender and race of Twitter users located in U.S. using advanced image
processing algorithms from Face++. Then, we investigate how different
demographic groups (i.e. male/female, Asian/Black/White) connect with other. We
quantify to what extent one group follow and interact with each other and the
extent to which these connections and interactions reflect in inequalities in
Twitter. Our analysis shows that users identified as White and male tend to
attain higher positions in Twitter, in terms of the number of followers and
number of times in user's lists. We hope our effort can stimulate the
development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web
Intelligence (WI'17). Leipzig, Germany. August 201
An analysis of interactions within and between extreme right communities in social media
Many extreme right groups have had an online presence for
some time through the use of dedicated websites. This has been accompanied by increased activity in social media websites in recent years, which may enable the dissemination of extreme right content to a wider
audience. In this paper, we present exploratory analysis of the activity of a selection of such groups on Twitter, using network representations based on reciprocal follower and mentions interactions. We find that stable communities of related users are present within individual country
networks, where these communities are usually associated with variants of extreme right ideology. Furthermore, we also identify the presence of international relationships between certain groups across geopolitical boundaries
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