19 research outputs found
Breaking the News: First Impressions Matter on Online News
A growing number of people are changing the way they consume news, replacing
the traditional physical newspapers and magazines by their virtual online
versions or/and weblogs. The interactivity and immediacy present in online news
are changing the way news are being produced and exposed by media corporations.
News websites have to create effective strategies to catch people's attention
and attract their clicks. In this paper we investigate possible strategies used
by online news corporations in the design of their news headlines. We analyze
the content of 69,907 headlines produced by four major global media
corporations during a minimum of eight consecutive months in 2014. In order to
discover strategies that could be used to attract clicks, we extracted features
from the text of the news headlines related to the sentiment polarity of the
headline. We discovered that the sentiment of the headline is strongly related
to the popularity of the news and also with the dynamics of the posted comments
on that particular news.Comment: The paper appears in ICWSM 201
The Lifecycles of Apps in a Social Ecosystem
Apps are emerging as an important form of on-line content, and they combine
aspects of Web usage in interesting ways --- they exhibit a rich temporal
structure of user adoption and long-term engagement, and they exist in a
broader social ecosystem that helps drive these patterns of adoption and
engagement. It has been difficult, however, to study apps in their natural
setting since this requires a simultaneous analysis of a large set of popular
apps and the underlying social network they inhabit.
In this work we address this challenge through an analysis of the collection
of apps on Facebook Login, developing a novel framework for analyzing both
temporal and social properties. At the temporal level, we develop a retention
model that represents a user's tendency to return to an app using a very small
parameter set. At the social level, we organize the space of apps along two
fundamental axes --- popularity and sociality --- and we show how a user's
probability of adopting an app depends both on properties of the local network
structure and on the match between the user's attributes, his or her friends'
attributes, and the dominant attributes within the app's user population. We
also develop models that show the importance of different feature sets with
strong performance in predicting app success.Comment: 11 pages, 10 figures, 3 tables, International World Wide Web
Conferenc
User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks
International audienceIn this paper we propose a procedure consisting of a first collection phase of social net- work messages, a subsequent user query selection, and finally a clustering phase, de- fined by extending the density-based DBSCAN algorithm, for performing a geographic and temporal exploration of a collection of items, in order to reveal and map their latent spatio-temporal structure. Specifically, both several geo-temporal distance measures and a density-based geo-temporal clustering algorithm are proposed. The approach can be applied to social messages containing an explicit geographic and temporal location. The algorithm usage is exemplified to identify geographic regions where many geotagged Twitter messages about an event of interest have been created, possibly in the same time period in the case of non-periodic events (aperiodic events), or at regular timestamps in the case of periodic events. This allows discovering the spatio-temporal periodic and aperiodic characteristics of events occurring in specific geographic areas, and thus increasing the awareness of decision makers who are in charge of territorial planning. Several case studies are used to illustrate the proposed procedure