6,910 research outputs found
Identifying communicator roles in Twitter
Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network
platforms nowadays, aiming to maximize user satisfaction along with other key
business objectives. Twitter is no exception. Despite the fact that Twitter
data has been extensively used to understand socioeconomic and political
phenomena and user behaviour, the implicit feedback provided by users on Tweets
through their engagements on the Home Timeline has only been explored to a
limited extent. At the same time, there is a lack of large-scale public social
network datasets that would enable the scientific community to both benchmark
and build more powerful and comprehensive models that tailor content to user
interests. By releasing an original dataset of 160 million Tweets along with
engagement information, Twitter aims to address exactly that. During this
release, special attention is drawn on maintaining compliance with existing
privacy laws. Apart from user privacy, this paper touches on the key challenges
faced by researchers and professionals striving to predict user engagements. It
further describes the key aspects of the RecSys 2020 Challenge that was
organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table
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
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
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