1,230 research outputs found
Recommending Items in Social Tagging Systems Using Tag and Time Information
In this work we present a novel item recommendation approach that aims at
improving Collaborative Filtering (CF) in social tagging systems using the
information about tags and time. Our algorithm follows a two-step approach,
where in the first step a potentially interesting candidate item-set is found
using user-based CF and in the second step this candidate item-set is ranked
using item-based CF. Within this ranking step we integrate the information of
tag usage and time using the Base-Level Learning (BLL) equation coming from
human memory theory that is used to determine the reuse-probability of words
and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data-sets gathered
from three social tagging systems (BibSonomy, CiteULike and MovieLens) show,
the usage of tag-based and time information via the BLL equation also helps to
improve the ranking and recommendation process of items and thus, can be used
to realize an effective item recommender that outperforms two alternative
algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure
Living Without a Mobile Phone: An Autoethnography
This paper presents an autoethnography of my experiences living without a
mobile phone. What started as an experiment motivated by a personal need to
reduce stress, has resulted in two voluntary mobile phone breaks spread over
nine years (i.e., 2002-2008 and 2014-2017). Conducting this autoethnography is
the means to assess if the lack of having a phone has had any real impact in my
life. Based on formative and summative analyses, four meaningful units or
themes were identified (i.e., social relationships, everyday work, research
career, and location and security), and judged using seven criteria for
successful ethnography from existing literature. Furthermore, I discuss factors
that allow me to make the choice of not having a mobile phone, as well as the
relevance that the lessons gained from not having a mobile phone have on the
lives of people who are involuntarily disconnected from communication
infrastructures.Comment: 12 page
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
In contrast to much previous work that has focused on location classification
of tweets restricted to a specific country, here we undertake the task in a
broader context by classifying global tweets at the country level, which is so
far unexplored in a real-time scenario. We analyse the extent to which a
tweet's country of origin can be determined by making use of eight
tweet-inherent features for classification. Furthermore, we use two datasets,
collected a year apart from each other, to analyse the extent to which a model
trained from historical tweets can still be leveraged for classification of new
tweets. With classification experiments on all 217 countries in our datasets,
as well as on the top 25 countries, we offer some insights into the best use of
tweet-inherent features for an accurate country-level classification of tweets.
We find that the use of a single feature, such as the use of tweet content
alone -- the most widely used feature in previous work -- leaves much to be
desired. Choosing an appropriate combination of both tweet content and metadata
can actually lead to substantial improvements of between 20\% and 50\%. We
observe that tweet content, the user's self-reported location and the user's
real name, all of which are inherent in a tweet and available in a real-time
scenario, are particularly useful to determine the country of origin. We also
experiment on the applicability of a model trained on historical tweets to
classify new tweets, finding that the choice of a particular combination of
features whose utility does not fade over time can actually lead to comparable
performance, avoiding the need to retrain. However, the difficulty of achieving
accurate classification increases slightly for countries with multiple
commonalities, especially for English and Spanish speaking countries.Comment: Accepted for publication in IEEE Transactions on Knowledge and Data
Engineering (IEEE TKDE
The Analogue Technology of S: Exploring Narrative Form and the Encoded Mystery of the Margin
The 2013 publication of S, J.J. Abrams’ and Doug Dorst’s “love letter to the written word,” represents a particular intervention in the debates surrounding the future of the book and the relationship between analogue and digital publication. In S we see how the analogue nature of this particular book drives much of the narrative structure of the text, indeed the physical presentation of the book informs much of the imaginative contents of the narrative. In this article I would like to consider the theoretic bounds of this novel and its form, from the question of marginal (and fragmented) writing that is evoked in the work of Jacques Derrida, to the importance of the medium and the message that it carries as described by Marshall McLuhan. One could furthermore consider the manner in which S integrates itself into the imagination of the reader both in textual and in literal terms in light of Umberto Eco’s notion of the “open work.
The afterlife of "Little Women" as a feminist text
MaestrĂa en InglĂ©s con orientaciĂłn en Literatura angloamericanaThis thesis closely examines the classic novel Little Women (1868) by Louisa May
Alcott and three contemporary reworkings: Hasta siempre, Mujercitas (2004) by Marcela
Serrano, The Little Women Letters (2012) by Gabrielle Donnelly and the manhwa Dear my
girls (2005 to 2012) by Kim Hee-Eun. In relation to Little Women’s hypertexts: pastiche,
sequel and adaptation, respectively, part of the analysis contemplates to what extent the texts
both pay homage to their nineteenth-century predecessor and refurbish it for a more
contemporary perspective from a postfeminist stance. Despite the fact that these texts were
created in different settings and times, they reveal how the patriarchal authority prevailing in
the past persists in this century. The main characters in each of them are strong and resilient
women trying to survive in a hostile world. These stories come together as a political appeal
for recognition to women who must be acknowledged and empowered.Fil: Lanzi, Elisabet Adriana. Universidad Nacional de CĂłrdoba. Facultad de Lenguas; Argentina
First Women, Second Sex: Gender Bias in Wikipedia
Contributing to history has never been as easy as it is today. Anyone with
access to the Web is able to play a part on Wikipedia, an open and free
encyclopedia. Wikipedia, available in many languages, is one of the most
visited websites in the world and arguably one of the primary sources of
knowledge on the Web. However, not everyone is contributing to Wikipedia from a
diversity point of view; several groups are severely underrepresented. One of
those groups is women, who make up approximately 16% of the current contributor
community, meaning that most of the content is written by men. In addition,
although there are specific guidelines of verifiability, notability, and
neutral point of view that must be adhered by Wikipedia content, these
guidelines are supervised and enforced by men.
In this paper, we propose that gender bias is not about participation and
representation only, but also about characterization of women. We approach the
analysis of gender bias by defining a methodology for comparing the
characterizations of men and women in biographies in three aspects: meta-data,
language, and network structure. Our results show that, indeed, there are
differences in characterization and structure. Some of these differences are
reflected from the off-line world documented by Wikipedia, but other
differences can be attributed to gender bias in Wikipedia content. We
contextualize these differences in feminist theory and discuss their
implications for Wikipedia policy.Comment: 10 pages, ACM style. Author's version of a paper to be presented at
ACM Hypertext 201
Characterization of Local Attitudes Toward Immigration Using Social Media
Migration is a worldwide phenomenon that may generate different reactions in
the population. Attitudes vary from those that support multiculturalism and
communion between locals and foreigners, to contempt and hatred toward
immigrants. Since anti-immigration attitudes are often materialized in acts of
violence and discrimination, it is important to identify factors that
characterize these attitudes. However, doing so is expensive and impractical,
as traditional methods require enormous efforts to collect data. In this paper,
we propose to leverage Twitter to characterize local attitudes toward
immigration, with a case study on Chile, where immigrant population has
drastically increased in recent years. Using semi-supervised topic modeling, we
situated 49K users into a spectrum ranging from in-favor to against
immigration. We characterized both sides of the spectrum in two aspects: the
emotions and lexical categories relevant for each attitude, and the discussion
network structure. We found that the discussion is mostly driven by Haitian
immigration; that there are temporal trends in tendency and polarity of
discussion; and that assortative behavior on the network differs with respect
to attitude. These insights may inform policy makers on how people feel with
respect to migration, with potential implications on communication of policy
and the design of interventions to improve inter-group relations.Comment: 8 pages, accepted at Latin American Web Congress 2019 (co-located
with The Web Conference
Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data
Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries
Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users
In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection
- …