Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter.
Ontology highlight
ABSTRACT:
SUBMITTER: Yoshida M
PROVIDER: S-EPMC8488035 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:This study explores the presence and actions of an electronic cigarette (e-cigarette) brand, Blu, on Twitter to observe how marketing messages are sent and diffused through the retweet (i.e., message forwarding) functionality. Retweet networks enable messages to reach additional Twitter users beyond the sender's local network. We follow messages from their origin through multiple retweets to identify which messages have more reach, and the different users who are exposed. METHODS:We collected three months of publicly available data from Twitter. A combination of techniques in social network analysis and content analysis were applied to determine the various networks of users who are exposed to e-cigarette messages and how the retweet network can affect which messages spread. RESULTS:The Blu retweet network expanded during the study period. Analysis of user profiles combined with network cluster analysis showed that messages of certain topics were only circulated within a community of e-cigarette supporters, while other topics spread further, reaching more general Twitter users who may not support or use e-cigarettes. CONCLUSIONS:Retweet networks can serve as proxy filters for marketing messages, as Twitter users decide which messages they will continue to diffuse among their followers. As certain e-cigarette messages extend beyond their point of origin, the audience being exposed expands beyond the e-cigarette community. Potential implications for health education campaigns include utilizing Twitter and targeting important gatekeepers or hubs that would maximize message diffusion.
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Project description:BACKGROUND:What impact gene loss has on the evolution of developmental processes, and how function shuffling has affected retained genes driving essential biological processes, remain open questions in the fields of genome evolution and EvoDevo. To investigate these problems, we have analyzed the evolution of the Wnt ligand repertoire in the chordate phylum as a case study. RESULTS:We conduct an exhaustive survey of Wnt genes in genomic databases, identifying 156 Wnt genes in 13 non-vertebrate chordates. This represents the most complete Wnt gene catalog of the chordate subphyla and has allowed us to resolve previous ambiguities about the orthology of many Wnt genes, including the identification of WntA for the first time in chordates. Moreover, we create the first complete expression atlas for the Wnt family during amphioxus development, providing a useful resource to investigate the evolution of Wnt expression throughout the radiation of chordates. CONCLUSIONS:Our data underscore extraordinary genomic stasis in cephalochordates, which contrasts with the liberal and dynamic evolutionary patterns of gene loss and duplication in urochordate genomes. Our analysis has allowed us to infer ancestral Wnt functions shared among all chordates, several cases of function shuffling among Wnt paralogs, as well as unique expression domains for Wnt genes that likely reflect functional innovations in each chordate lineage. Finally, we propose a potential relationship between the evolution of WntA and the evolution of the mouth in chordates.
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Project description:People tend to interpret political information in a manner that confirms their prior beliefs, a cognitive bias that contributes to rising political polarization. In this study, we combined functional magnetic resonance imaging with semantic content analyses to investigate the neural mechanisms that underlie the biased processing of real-world political content. We scanned American participants with conservative-leaning or liberal-leaning immigration attitudes while they watched news clips, campaign ads, and public speeches related to immigration policy. We searched for evidence of "neural polarization": activity in the brain that diverges between people who hold liberal versus conservative political attitudes. Neural polarization was observed in the dorsomedial prefrontal cortex (DMPFC), a brain region associated with the interpretation of narrative content. Neural polarization in the DMPFC intensified during moments in the videos that included risk-related and moral-emotional language, highlighting content features most likely to drive divergent interpretations between conservatives and liberals. Finally, participants whose DMPFC activity closely matched that of the average conservative or the average liberal participant were more likely to change their attitudes in the direction of that group's position. Our work introduces a multimethod approach to study the neural basis of political cognition in naturalistic settings. Using this approach, we characterize how political attitudes biased information processing in the brain, the language most likely to drive polarized neural responses, and the consequences of biased processing for attitude change. Together, these results shed light on the psychological and neural underpinnings of how identical information is interpreted differently by conservatives and liberals.
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Project description:The 2008 financial crisis unveiled the intrinsic failures of the financial system as we know it. As a consequence, impact investing started to receive increasing attention, as evidenced by the high market growth rates. The goal of impact investment is to generate social and environmental impact alongside a financial return. In this paper we identify the main players in the sector and how they interact and communicate with each other. We use Twitter as a proxy of the impact investing market, and analyze relevant tweets posted over a period of ten months. We apply network, contents and sentiment analysis on the acquired dataset.Our study shows that Twitter users exhibit favourable leaning (predominantly neutral or positive) towards impact investing. Retweet communities are decentralised and include users from a variety of sectors. Despite some basic common vocabulary used by all retweet communities identified, the vocabulary and the topics discussed by each community vary largely. We note that an additional effort should be made in raising awareness about the sector, especially by policymakers and media outlets. The role of investors and the academia is also discussed, as well as the emergence of hybrid business models within the sector and its connections to the tech industry. This paper extends our previous study, one of the first analyses of Twitter activities in the impact investing market.
| S-EPMC6214330 | biostudies-other
Project description:Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette-related content on Twitter.The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette-related topics into distinct categories.We collected approximately 11.5 million e‑cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types.Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F1 score gain of 10.6%-beyond metadata features alone.This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.
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Project description:BackgroundBoth negative and positive associations between social networking service (SNS) usage and mental health have been suggested by previous studies; however, their differences by type of SNS and age remain unclear. We addressed this issue based on the frequency of traditional communication such as face-to-face and non-face-to-face communication (e.g., phone, email, and letters).MethodsIn total, 8,576 individuals participated, including 2,543 aged 18-39, 3,048 aged 40-64, and 2,985 aged over 65 years. They were asked to indicate their frequency of SNS usage, both for posting and checking, of LINE (a popular message application in Japan), Facebook, Twitter, and Instagram, with frequent usage defined as posting or checking more than a few times a week. To determine mental health status, WHO-5 (i.e., well-being), K6 (i.e., distress symptoms), and feelings of loneliness were assessed. Multiple and logistic regression analyses were adjusted for the frequency of traditional communication. To avoid type 1 error, a Bonferroni correction of p ≤ 0.002 was applied in the regression models (p = 0.05/18, a number of regression models).ResultsThe most frequently used SNS across the three age groups was LINE; frequent usage (both posting and checking) among older adults was independently associated with better well-being. Frequent posting on Facebook was associated with better well-being in middle-aged adults. Young adults who frequently checked on Instagram showed a tendency toward better well-being and lower distress symptoms. On the contrary, frequent usage of Twitter was associated with distress symptoms or feelings of loneliness across all three age groups.ConclusionsWe found generational and SNS-type-dependent negative and positive associations between SNS use and mental health, indicating the possible influences of SNS use and the importance of non-SNS communication.
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Project description:Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading.
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Project description:Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100-200 stable relationships. Thus, the 'economy of attention' is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.
| S-EPMC3149601 | biostudies-literature
Project description:BackgroundPharmacists are often depicted as dispensers of medication as opposed to health professionals who provide patient care. Our objective was to identify how pharmacists' roles were portrayed in images on Twitter for World Pharmacists Day (WPD) 2020.MethodsA qualitative content analysis was undertaken to evaluate the images of pharmacists on WPD. Tweets were included that had a photo or image, used 1 of 5 WPD hashtags and were posted between September 24 and 26, 2020. Thematic analysis was performed independently by 2 investigators. Tweets were categorized into 5 groups: 1) drug distribution roles, 2) patient care roles, 3) no described roles, 4) not professional/unprofessional roles and 5) not relevant. Subgroup analysis was performed for the users who posted the tweets.ResultsOf the 970 tweets analyzed, only 11% of the overall tweets portrayed the patient care roles of pharmacists, whereas 51% portrayed drug distribution roles and 29% did not describe any particular roles of pharmacists. These proportions were similar between subgroups of tweeters.DiscussionWPD is intended to promote and advocate for the pharmacy profession. The results of our study show poor messaging of pharmacists' roles and reflect a missed opportunity to showcase the full scope of pharmacy practice.ConclusionBased on the images tweeted on WPD 2020, the diversity of pharmacists' practice as skilled health care practitioners was lost in a sea of tweets portraying pharmacists' roles in drug distribution. We hope that pharmacists and pharmacy organizations will awaken to this self-induced problem for WPD 2021. Can Pharm J (Ott) 2021;154(5):xx-xx.
| S-EPMC8408907 | biostudies-literature