摘要 :
In order to achieve the concept of ubiquitous computing, popularized by Mark Weiser, is necessary to sense the environment. One alternative is use traditional wireless sensor networks (WSNs). However, WSNs have their limitations, ...
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In order to achieve the concept of ubiquitous computing, popularized by Mark Weiser, is necessary to sense the environment. One alternative is use traditional wireless sensor networks (WSNs). However, WSNs have their limitations, for instance in the sensing of large areas, such as metropolises, because it incurs in high costs to build and maintain such networks. The ubiquity of smart phones associated with the adoption of social media websites, forming what is called participatory sensing systems (PSSs), enables unprecedented opportunities to sense the environment. Particularly, the data sensed by PSSs is very interesting to study city dynamics and urban social behavior. The goal of this work is to survey approaches and models applied to PSSs data aiming the study city dynamics and urban social behavior. Besides that it is also an objective of this work discuss some of the challenges and opportunities when using social media as a source of sensing.
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摘要 :
In order to achieve the concept of ubiquitous computing, popularized by Mark Weiser, is necessary to sense the environment. One alternative is use traditional wireless sensor networks (WSNs). However, WSNs have their limitations, ...
展开
In order to achieve the concept of ubiquitous computing, popularized by Mark Weiser, is necessary to sense the environment. One alternative is use traditional wireless sensor networks (WSNs). However, WSNs have their limitations, for instance in the sensing of large areas, such as metropolises, because it incurs in high costs to build and maintain such networks. The ubiquity of smart phones associated with the adoption of social media websites, forming what is called participatory sensing systems (PSSs), enables unprecedented opportunities to sense the environment. Particularly, the data sensed by PSSs is very interesting to study city dynamics and urban social behavior. The goal of this work is to survey approaches and models applied to PSSs data aiming the study city dynamics and urban social behavior. Besides that it is also an objective of this work discuss some of the challenges and opportunities when using social media as a source of sensing.
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Twitter was widely used during the 2020 U.S. election to disseminate claims of election fraud. As a result, a number of works have examined this phenomenon from a variety of perspectives. However, none of them focus on analyzing t...
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Twitter was widely used during the 2020 U.S. election to disseminate claims of election fraud. As a result, a number of works have examined this phenomenon from a variety of perspectives. However, none of them focus on analyzing topics behind the general fraud claims and associating them with user communities. To fill this gap, we propose to uncover and characterize groups of Twitter users engaging in discussions about election fraud claims during the 2020 U.S. election using a large dataset that spans seven weeks during this period. To accomplish this, we model a sequence of co-retweet networks and employ a backbone extraction method that controls for inherent traits of social media applications, particularly, user activity levels and the popularity of tweets (which together generate many spurious edges in the network), thus allowing us to reveal topics of tweets that lead users to retweet them. After extracting the backbones, we identify user groups representative of the communities present in the network backbones and finally analyze the topics behind the retweeted tweets to understand how they contributed to the spread of fraud claims at that time. Our main results show that (ⅰ) our approach uncovers better-structured communities than the original network in terms of users spreading discussions about fraud; and (ⅱ) these users discuss 25 topics with specific psycholinguistic and temporal characteristics.
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Some countries impose strict regulations regarding the distribution of electoral advertising during election periods. This is the case of Brazil, where electoral ads distributed before a predetermined period (called early ad) are ...
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Some countries impose strict regulations regarding the distribution of electoral advertising during election periods. This is the case of Brazil, where electoral ads distributed before a predetermined period (called early ad) are prohibited by law. Whereas the enforcement of such regulation on traditional mass media technologies (e.g., radio and TV) is common practice in the country, the same is a very challenging task for content shared on social media platforms, mostly due to the lack of proper tools to automatically identify content containing (early) electoral ads. This study aims to develop fundamental knowledge about characteristics of textual content containing early ads shared on Twitter, so as to drive the future design of effective detection tools. We offer a broad characterization of the textual content associated with a set of early electoral ads shared on Twitter in pre-election periods of three recent elections in Brazil, comparing their textual properties with those of other (non ads) tweets. Our main findings are that ads tend to have a negative or neutral sentiment, a certain syntactic structure, while most tend to explicitly mention a candidate or party to be chosen or avoided.
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Recent studies have shown that group communication on WhatsApp plays a significant role to foster information dissemination at large, with evidence of its use for misinformation campaigns. We analyze more than 40K audio messages s...
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Recent studies have shown that group communication on WhatsApp plays a significant role to foster information dissemination at large, with evidence of its use for misinformation campaigns. We analyze more than 40K audio messages shared in over 364 publicly accessible groups in Brazil, covering six months of great social mobilization in the country. We identify the presence of misinformation in these audios by relying on previously checked facts. Our study focuses on content and propagation properties of audio misinformation, contrasting them with unchecked content as well as with prior findings of misinformation in other media types. We also rely on a set of volunteers to perform a qualitative analysis of the audios. We observed that audios with misinformation had a higher presence of negative emotions and also often used phrases in the future tense and talked directly to the listener. Moreover, audios with misinformation tend to spread quicker than unchecked content and last significantly longer in the network. The speaker's tone from the audios with misinformation was also considered less friendly and natural than the unchecked ones. Our study contributes to the literature by focusing on a media type that is gaining mainstream popularity recently, and, as we show here, is being used as vessel for misinformation spread.
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摘要 :
Recent studies have shown that group communication on WhatsApp plays a significant role to foster information dissemination at large, with evidence of its use for misinformation campaigns. We analyze more than 40K audio messages s...
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Recent studies have shown that group communication on WhatsApp plays a significant role to foster information dissemination at large, with evidence of its use for misinformation campaigns. We analyze more than 40K audio messages shared in over 364 publicly accessible groups in Brazil, covering six months of great social mobilization in the country. We identify the presence of misinformation in these audios by relying on previously checked facts. Our study focuses on content and propagation properties of audio misinformation, contrasting them with unchecked content as well as with prior findings of misinformation in other media types. We also rely on a set of volunteers to perform a qualitative analysis of the audios. We observed that audios with misinformation had a higher presence of negative emotions and also often used phrases in the future tense and talked directly to the listener. Moreover, audios with misinformation tend to spread quicker than unchecked content and last significantly longer in the network. The speaker's tone from the audios with misinformation was also considered less friendly and natural than the unchecked ones. Our study contributes to the literature by focusing on a media type that is gaining mainstream popularity recently, and, as we show here, is being used as vessel for misinformation spread.
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How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implicati...
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How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.
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摘要 :
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implicati...
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How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.
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摘要 :
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implicati...
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How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the PHOENIX-R model which captures the popularity dynamics of individual objects. PHOENIX-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.
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Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tag-b...
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Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tag-based systems, tag recommendation services can assist a user in the assignment of tags to objects and help consolidate the vocabulary of tags across users. A promising approach for tag recommendation is to exploit the co-occurrence of tags. However, these methods are challenged by the huge size of the tag vocabulary, either because (1) the computational complexity may increase exponentially with the number of tags or (2) the score associated with each tag may become distorted since different tags may operate in different scales and the scores are not directly comparable. In this paper we propose a novel method that recommends tags on a demand-driven basis according to an initial set of tags applied to an object. It reduces the space of possible solutions, so that its complexity increases polynomially with the size of the tag vocabulary. Further, the score of each tag is calibrated using an entropy minimization approach which corrects possible distortions and provides more precise recommendations. We conducted a systematic evaluation of the proposed method using three types of media: audio, bookmarks and video. The experimental results show that the proposed method is fast and boosts recommendation quality on different experimental scenarios. For instance, in the case of a popular audio site it provides improvements in precision (p@5) ranging from 6.4% to 46.7% (depending on the number of tags given as input), outperforming a recently proposed co-occurrence based tag recommendation method.
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