Social media and flu: Media Twitter accounts as agenda setters

Yun, Gi Woong; Morin, David; Park, Sanghee; Joa, Claire Youngnyo; Labbe, Brett R.; Lim, Jae-Han; Lee, Soo Young; Hyun, Daewon

doi:10.1016/j.ijmedinf.2016.04.009

Cited by 51 publications

(34 citation statements)

References 18 publications

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“…These data are usually publicly available and voluntarily generated by users. A large body of research has exploited this available information for inferring both social aspects (e.g., influenza trends [68], sentiment in the stock market [69] or unemployment rates [70]) and individual characteristics (e.g., personality [71], location [72], ethnicity [73], or political affiliation [74]).…”

Section: Characterizing and Interlinking User Profiles In Social Netwmentioning

confidence: 99%

Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.

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Section: Characterizing and Interlinking User Profiles In Social Netwmentioning

confidence: 99%

Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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“…While this qualitative analysis does not appear to have undergone peer review, it suggests a pro‐vaccination narrative in mainstream media characterized by fear‐inducing storylines that highlighted the dangers of not vaccinating alongside arguments stressing the safety and efficacy of vaccinations (Palladino, ). For example, Jimmy Kimmel made vaccine education a focal point of his TV program following the outbreak, helping bring the issue into many American homes (Bradley, ; Yun et al., ).…”

Section: Literature Reviewmentioning

confidence: 99%

It's Complicated: The 2014–2015 U.S. Measles Outbreak and Parents’ Vaccination Beliefs, Confidence, and Intentions

2018

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While it seems intuitive that highly visible vaccine-preventable disease outbreaks should impact perceptions of disease risk and facilitate vaccination, few empirical studies exist to confirm or dispel these beliefs. This study investigates the impact of the 2014-2015 Disneyland measles outbreak on parents' vaccination attitudes and future vaccination intentions. The analysis relies on a pair of public opinion surveys of American parents with at least one child under the age of six (N = 1,000 across each survey). Controlling for basic demographics, we found higher levels of reported confidence in the safety and efficacy of childhood vaccinations in our follow-up data collection. However, this confidence was also accompanied by elevated levels of concern toward childhood vaccines among American parents. We then examined how different subgroups in the population scored on these measures before and after the outbreak. We found that parents with high levels of interest in the topic of vaccines and a child who is not fully upto date with the recommended vaccination schedule reported more supportive attitudes toward vaccines. However, future intentions to follow the recommended vaccination schedule were not positively impacted by the outbreak. Possible explanations for these results and implications for vaccination outreach are discussed.

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Section: Related Workmentioning

confidence: 99%

“…Many sampling techniques were studied ranging from topical [11,19] to user-based approaches [12]. The first set of techniques is topic-based sampling, where specific keywords or hashtags are applied to collect tweets through Twitter API [6,20].As an example, Kumar and Geethakumari [19] used different keywords to collect tweets related to natural calamities and political event for the purpose of detecting misinformation in Twitter. This group of sampling limits the study around the content of shared topics, which are not scalable to many applications.…”

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confidence: 99%

Activity-based Twitter sampling for content-based and user-centric prediction models

Aghababaei

Makrehchi

2017

Hum. Cent. Comput. Inf. Sci.

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BackgroundTwitter's public and open nature provides great opportunities for its users to actively participate in sharing their opinions and produce high quality content that is reflective of their tendencies and preferences in their day-to-day life [1]. This vast amount of publicly available user-generated content is applied to many applications ranging from tracking human social behavior [2][3][4], detecting events of interest [5][6][7], to smart business [8] where domain knowledge is collected through social media. These studies are either concerned with pulling Twitter and aggregating tweets as bulk or tracking historical tweets over time in order to find meaningful patterns for targeted events. The main challenge of the former studies is the limitation of the Twitter API in accessing only 1% of all existing tweets. However, despite this limitation, the latter studies are concerned with retrieving historical timelines of users.To tackle the above issues of retrieving more tweets beyond the 1% threshold and obtaining historical timelines, topic-based sampling and REST API are both shown to Abstract Increasingly more applications rely on crowd-sourced data from social media. Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical data. Nevertheless, determining the subset of "credible" users is crucial. While the majority of sampling approaches focus on individual static networks, dynamic user activity over time is usually not considered, which may result in activity gaps in the collected data. Models based on noisy and missing data can significantly degrade in performance. In this study, we demonstrate how to sample Twitter users in order to produce more credible data for temporal prediction models. We present an activity-based sampling approach where users are selected based on their historical activities in Twitter. The predictability of the collected content from activity-based and random sampling is compared in a content-based and user-centric temporal model. The results indicate the importance of an activityoriented sampling method for the acquisition of more credible content for temporal models.Keywords: Twitter sampling, Temporal prediction models, Historical timelines, User activity, Activity-based sampling Open Access© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Aghababaei and Makrehchi Hum. Cent. Comput. Inf. Sci. (2017) Page 2 of 20 Aghababaei and Makrehchi Hum. Cent. Comput. Inf. Sci. (2017) 7:3 be more effective [9,10]. In topic-based sampling [11], a set of specific keywords or hashtags are applied to collect tweets through the search API...

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Social media and flu: Media Twitter accounts as agenda setters

Cited by 51 publications

References 18 publications

Predicting Reputation in the Sharing Economy with Twitter Social Data

Predicting Reputation in the Sharing Economy with Twitter Social Data

It's Complicated: The 2014–2015 U.S. Measles Outbreak and Parents’ Vaccination Beliefs, Confidence, and Intentions

Activity-based Twitter sampling for content-based and user-centric prediction models

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