65,209 research outputs found
Is That Twitter Hashtag Worth Reading
Online social media such as Twitter, Facebook, Wikis and Linkedin have made a
great impact on the way we consume information in our day to day life. Now it
has become increasingly important that we come across appropriate content from
the social media to avoid information explosion. In case of Twitter, popular
information can be tracked using hashtags. Studying the characteristics of
tweets containing hashtags becomes important for a number of tasks, such as
breaking news detection, personalized message recommendation, friends
recommendation, and sentiment analysis among others.
In this paper, we have analyzed Twitter data based on trending hashtags,
which is widely used nowadays. We have used event based hashtags to know users'
thoughts on those events and to decide whether the rest of the users might find
it interesting or not. We have used topic modeling, which reveals the hidden
thematic structure of the documents (tweets in this case) in addition to
sentiment analysis in exploring and summarizing the content of the documents. A
technique to find the interestingness of event based twitter hashtag and the
associated sentiment has been proposed. The proposed technique helps twitter
follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium
on Women in Computing and Informatics (WCI-2015
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Towards a social learning space for open educational resources
We identify a number of meanings of “Open”, as part of the motivating rationale for a social media space tuned for learning, called SocialLearn. We discuss why online social learning seems to be emerging so strongly at this point, explore features of social learning, and identify some of the dimensions that we believe characterize the social learning design space, before describing the emerging design concept and implementation
Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources
Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information
There has been much effort on studying how social media sites, such as
Twitter, help propagate information in different situations, including
spreading alerts and SOS messages in an emergency. However, existing work has
not addressed how to actively identify and engage the right strangers at the
right time on social media to help effectively propagate intended information
within a desired time frame. To address this problem, we have developed two
models: (i) a feature-based model that leverages peoples' exhibited social
behavior, including the content of their tweets and social interactions, to
characterize their willingness and readiness to propagate information on
Twitter via the act of retweeting; and (ii) a wait-time model based on a user's
previous retweeting wait times to predict her next retweeting time when asked.
Based on these two models, we build a recommender system that predicts the
likelihood of a stranger to retweet information when asked, within a specific
time window, and recommends the top-N qualified strangers to engage with. Our
experiments, including live studies in the real world, demonstrate the
effectiveness of our work
CHORUS Deliverable 4.4: Report of the 2nd CHORUS Conference
The Second CHORUS Conference and third Yahoo! Research Workshop on the Future of Web Search was held during April 4-5, 2008, in Granvalira, Andorra to discuss future directions in multi-medial information access and other specialised topics in the near future of retrieval. Attendance was at capacity, with 97 participants from 11 countries and 3 continents
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