Concept: Social media
We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or ‘boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personality.
- Proceedings of the National Academy of Sciences of the United States of America
- Published about 2 years ago
The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15–where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades' size.
The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories-e.g., chemtrails, reptilians or the Illuminati-are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives-i.e. main stream scientific and conspiracy news-are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users' content selection, we conclude our analysis measuring how users respond to 4,709 troll information-i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.
Altmetric measurements derived from the social web are increasingly advocated and used as early indicators of article impact and usefulness. Nevertheless, there is a lack of systematic scientific evidence that altmetrics are valid proxies of either impact or utility although a few case studies have reported medium correlations between specific altmetrics and citation rates for individual journals or fields. To fill this gap, this study compares 11 altmetrics with Web of Science citations for 76 to 208,739 PubMed articles with at least one altmetric mention in each case and up to 1,891 journals per metric. It also introduces a simple sign test to overcome biases caused by different citation and usage windows. Statistically significant associations were found between higher metric scores and higher citations for articles with positive altmetric scores in all cases with sufficient evidence (Twitter, Facebook wall posts, research highlights, blogs, mainstream media and forums) except perhaps for Google+ posts. Evidence was insufficient for LinkedIn, Pinterest, question and answer sites, and Reddit, and no conclusions should be drawn about articles with zero altmetric scores or the strength of any correlation between altmetrics and citations. Nevertheless, comparisons between citations and metric values for articles published at different times, even within the same year, can remove or reverse this association and so publishers and scientometricians should consider the effect of time when using altmetrics to rank articles. Finally, the coverage of all the altmetrics except for Twitter seems to be low and so it is not clear if they are prevalent enough to be useful in practice.
The traditional vertical system of sharing information from sources of scientific authority passed down to the public through local health authorities and clinicians risks being made obsolete by emerging technologies that facilitate rapid horizontal information sharing. The rise of Public Health 2.0 requires professional acknowledgment that a new and substantive forum of public discourse about public health exists on social media, such as forums, blogs, Facebook, and Twitter.
Health care professionals, patients, caregivers, family, friends, and other supporters are increasingly joining online health communities to share information and find support. But social Web (Web 2.0) technology alone does not create a successful online community. Building and sustaining a successful community requires an enabler and strategic community management. Community management is more than moderation. The developmental life cycle of a community has four stages: inception, establishment, maturity, and mitosis. Each stage presents distinct characteristics and management needs. This paper describes the community management strategies, resources, and expertise needed to build and maintain a thriving online health community; introduces some of the challenges; and provides a guide for health organizations considering this undertaking. The paper draws on insights from an ongoing study and observation of online communities as well as experience managing and consulting a variety of online health communities. Discussion includes effective community building practices relevant to each stage, such as outreach and relationship building, data collection, content creation, and other proven techniques that ensure the survival and steady growth of an online health community.
Our reputation is important to us; we’ve experienced natural selection to care about our reputation. Recently, the neural processing of gains in reputation (positive social feedback concerning one’s character) has been shown to occur in the human ventral striatum. It is still unclear, however, how individual differences in the processing of gains in reputation may lead to individual differences in real-world behavior. For example, in the real-world, one way that people currently maintain their reputation is by using social media websites, like Facebook. Furthermore, Facebook use consists of a social comparison component, where users observe others' behavior and can compare it to their own. Therefore, we hypothesized a relationship between the way the brain processes specifically self-relevant gains in reputation and one’s degree of Facebook use. We recorded functional neuroimaging data while participants received gains in reputation, observed the gains in reputation of another person, or received monetary reward. We demonstrate that across participants, when responding to gains in reputation for the self, relative to observing gains for others, reward-related activity in the left nucleus accumbens predicts Facebook use. However, nucleus accumbens activity in response to monetary reward did not predict Facebook use. Finally, a control step-wise regression analysis showed that Facebook use primarily explains our results in the nucleus accumbens. Overall, our results demonstrate how individual sensitivity of the nucleus accumbens to the receipt of self-relevant social information leads to differences in real-world behavior.
Social media has created networked communication channels that facilitate interactions and allow information to proliferate within professional academic communities as well as in informal social circumstances. A significant contemporary discussion in the field of science communication is how scientists are using (or might use) social media to communicate their research. This includes the role of social media in facilitating the exchange of knowledge internally within and among scientific communities, as well as externally for outreach to engage the public. This study investigates how a surveyed sample of 587 scientists from a variety of academic disciplines, but predominantly the academic life sciences, use social media to communicate internally and externally. Our results demonstrate that while social media usage has yet to be widely adopted, scientists in a variety of disciplines use these platforms to exchange scientific knowledge, generally via either Twitter, Facebook, LinkedIn, or blogs. Despite the low frequency of use, our work evidences that scientists perceive numerous potential advantages to using social media in the workplace. Our data provides a baseline from which to assess future trends in social media use within the science academy.
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.