Portuguese geolocated tweets

Brogueira, Gaspar; Batista, Fernando; Carvalho, João Paulo; Moniz, Helena

doi:10.1145/2618168.2618200

Cited by 4 publications

(6 citation statements)

References 1 publication

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“… Taking into consideration that currently there are 500 million tweets per day 5 , it is theoretically possible to retrieve up to 5 million tweets per day using the Streaming API from a single user account;  Previous works (Brogueira, 2014a(Brogueira, , 2014b have shown that in 2014, 60000 geolocated tweets were produced per day, in Portugal and using (European) Portuguese, i.e., almost 2 orders of magnitude lower than the 5 million Streaming API daily limits.…”

Section: Geolocated Data Collectionmentioning

confidence: 99%

“…Although 10,687 tweets out of a universe of 3,309,468 tweets, seems a low number, this can be explained by the absence in the database of the major media players, and also by the fact that the majority of Twitter users in Portugal are young teenagers (Brogueira, 2014a(Brogueira, , 2014b) that usually do not show any special interest in political matters.…”

Section: Application Examplementioning

confidence: 99%

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MISNIS: An intelligent platform for twitter topic mining

Carvalho

Rosa

Brogueira

et al. 2017

Expert Systems with Applications

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Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization "mechanism" is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS-Intelligent Mining of Public Social Networks' Influence in Society-that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations.

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Section: Geolocated Data Collectionmentioning

confidence: 99%

Section: Application Examplementioning

confidence: 99%

MISNIS: An intelligent platform for twitter topic mining

Carvalho

Rosa

Brogueira

et al. 2017

Expert Systems with Applications

Self Cite

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“…The data has been restricted to English geolocated tweets, either from the United States or from the United Kingdom, totaling 296506 unique users. The Portuguese dataset is a subset of the data described in Brogueira et al [5], and corresponds to a database of Portuguese users, restricted by users that have tweeted during October of 2014 in Portuguese language, and geolocated in the Portuguese mainland.…”

Section: Data and Featuresmentioning

confidence: 99%

“…This user-generated information provides clues about users' opinions, daily routines, reaction to events, among other. Twitter, with about 500 million user-generated tweets per day, provides an opportunity for social networking studies [4], and has become the subject of studies seeking to understand public opinion [7]. Unlike other social networks, a user name is the only required field when creating a Twitter profile.…”

Section: Introductionmentioning

confidence: 99%

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Using Unstructured Profile Information for Gender Classification of Portuguese and English Twitter Users

Vicente

Carvalho

Batista

2015

Communications in Computer and Information Science

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This paper reports experiments on automatically detecting the gender of Twitter users, based on unstructured information found on their Twitter profile. A set of features previously proposed is evaluated on two datasets of English and Portuguese users, and their performance is assessed using several supervised and unsupervised approaches, including Naive Bayes variants, Logistic Regression, Support Vector Machines, Fuzzy c-Means clustering, and k-means. Results show that features perform well in both languages separately, but even best results were achieved when combining both languages. Supervised approaches reached 97.9% accuracy, but Fuzzy c-Means also proved suitable for this task achieving 96.4% accuracy.

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