2,034 research outputs found
A Personalized Travel Recommendation System Using Social Media Analysis
Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model
Patterns of implicit and non-follower retweet propagation: investigating the role of applications and hashtags
Existing literature on retweets seems to focus mainly on retweets created using explicit, formal retweeting mechanisms, such as Twitter's own native retweet function, and the prefixing of the terms 'RT' or 'via' in front of copied tweets. However, retweets can also be made using implicit, informal mechanisms. These include tweet replies and other mechanisms, which use neither the native nor RT/via mechanisms, but their content and timelines suggest the likelihood of being a retweet. Moreover, retweets can also occur with or without a defined follower/following network path between a tweet originator and a retweeter. This paper presents an initial taxonomy of propagation based on seven different ways a tweet may spread: native, native non-follower, RT/Via, RT/Via non-follower, replies, non-follower replies and other implicit 'retweets'. An experiment has examined this new model, by investigating where tweets containing URLs from the domains of online petitions, charity fundraisers, news portals, and YouTube videos can be classified into the seven different categories. When including other implicit 'retweets', more than 50% of all the retweets found across all four domains were classified as implicit retweets, while more than 79% of all retweets were made by non-followers. More work needs to be done on the composition of other implicit 'retweets'. Initial investigations found hashtags in 99-100% of these tweets, suggesting that retweeting using conventional mechanisms may not be the main method that URLs get propagated across microblogs
Characterizing Geo-located Tweets in Brazilian Megacities
This work presents a framework for collecting, processing and mining
geo-located tweets in order to extract meaningful and actionable knowledge in
the context of smart cities. We collected and characterized more than 9M tweets
from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We
performed topic modeling using the Latent Dirichlet Allocation model to produce
an unsupervised distribution of semantic topics over the stream of geo-located
tweets as well as a distribution of words over those topics. We manually
labeled and aggregated similar topics obtaining a total of 29 different topics
across both cities. Results showed similarities in the majority of topics for
both cities, reflecting similar interests and concerns among the population of
Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more
predominant in one of the cities
Characterizing Geo-located Tweets in Brazilian Megacities
This work presents a framework for collecting, processing and mining
geo-located tweets in order to extract meaningful and actionable knowledge in
the context of smart cities. We collected and characterized more than 9M tweets
from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We
performed topic modeling using the Latent Dirichlet Allocation model to produce
an unsupervised distribution of semantic topics over the stream of geo-located
tweets as well as a distribution of words over those topics. We manually
labeled and aggregated similar topics obtaining a total of 29 different topics
across both cities. Results showed similarities in the majority of topics for
both cities, reflecting similar interests and concerns among the population of
Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more
predominant in one of the cities
Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior
This paper analyzes the tweeting behavior of 37 astrophysicists on Twitter
and compares their tweeting behavior with their publication behavior and
citation impact to show whether they tweet research-related topics or not.
Astrophysicists on Twitter are selected to compare their tweets with their
publications from Web of Science. Different user groups are identified based on
tweeting and publication frequency. A moderate negative correlation (p=-0.390*)
is found between the number of publications and tweets per day, while retweet
and citation rates do not correlate. The similarity between tweets and
abstracts is very low (cos=0.081). User groups show different tweeting behavior
such as retweeting and including hashtags, usernames and URLs. The study is
limited in terms of the small set of astrophysicists. Results are not
necessarily representative of the entire astrophysicist community on Twitter
and they most certainly do not apply to scientists in general. Future research
should apply the methods to a larger set of researchers and other scientific
disciplines. To a certain extent, this study helps to understand how
researchers use Twitter. The results hint at the fact that impact on Twitter
can neither be equated with nor replace traditional research impact metrics.
However, tweets and other so-called altmetrics might be able to reflect other
impact of scientists such as public outreach and science communication. To the
best of our knowledge, this is the first in-depth study comparing researchers'
tweeting activity and behavior with scientific publication output in terms of
quantity, content and impact.Comment: 14 pages, 5 figures, 7 table
Characterizing Information Diets of Social Media Users
With the widespread adoption of social media sites like Twitter and Facebook,
there has been a shift in the way information is produced and consumed.
Earlier, the only producers of information were traditional news organizations,
which broadcast the same carefully-edited information to all consumers over
mass media channels. Whereas, now, in online social media, any user can be a
producer of information, and every user selects which other users she connects
to, thereby choosing the information she consumes. Moreover, the personalized
recommendations that most social media sites provide also contribute towards
the information consumed by individual users. In this work, we define a concept
of information diet -- which is the topical distribution of a given set of
information items (e.g., tweets) -- to characterize the information produced
and consumed by various types of users in the popular Twitter social media. At
a high level, we find that (i) popular users mostly produce very specialized
diets focusing on only a few topics; in fact, news organizations (e.g.,
NYTimes) produce much more focused diets on social media as compared to their
mass media diets, (ii) most users' consumption diets are primarily focused
towards one or two topics of their interest, and (iii) the personalized
recommendations provided by Twitter help to mitigate some of the topical
imbalances in the users' consumption diets, by adding information on diverse
topics apart from the users' primary topics of interest.Comment: In Proceeding of International AAAI Conference on Web and Social
Media (ICWSM), Oxford, UK, May 201
Re-ranking Real-time Web Tweets to Find Reliable and Influential Twitterers
Twitter is a powerful social media tool to share information on different topics around the world. Following different users/accounts is the most effective way to get information propagated in Twitter. Due to Twitter's limited searching and lack of navigation support, searching Twitter is not easy and requires effort to find reliable information. This thesis proposed a new methodology to rank tweets based on their authority with the goal of aiding users identifying influential Twitterers. This methodology, HIRKM rank, is influenced by PageRank, Alexa Rank, original tweet or a retweet and the use of hash tags to determine the authorisation of each tweet. This method is applied to rank TREC 2011 microblogging dataset which contains over 16 million tweets based on 50 predefined topics. The results are a list of tweets presented in a descending order based on their authorities which are relevant to the users search queries and will be evaluated using TREC’s official golden standard for the microblogging dataset
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