ResearchPad - mass-media https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Ten simple rules for designing learning experiences that involve enhancing computational biology Wikipedia articles]]> https://www.researchpad.co/article/elastic_article_14536 <![CDATA[Is bad news on TV tickers good news? The effects of voiceover and visual elements in video on viewers’ assessment]]> https://www.researchpad.co/article/N6a76f847-8cb5-45f6-9e66-865fc26387f0

In our experiment, we tested how exposure to a mock televised news segment, with a systematically manipulated emotional valence of voiceover, images and TV tickers (in the updating format) impacts viewers’ perception. Subjects (N = 603) watched specially prepared professional video material which portrayed the story of a candidate for local mayor. Following exposure to the video, subjects assessed the politician in terms of competence, sociability, and morality.

Results showed that positive images improved the assessment of the politician, whereas negative images lowered it. In addition, unexpectedly, positive tickers led to a negative assessment, and negative ones led to more beneficial assessments. However, in a situation of inconsistency between the voiceover and information provided on visual add-ons, additional elements are apparently ignored, especially when they are negative and the narrative is positive. We then discuss the implications of these findings.

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<![CDATA[A season for all things: Phenological imprints in Wikipedia usage and their relevance to conservation]]> https://www.researchpad.co/article/5c882408d5eed0c4846395ce

Phenology plays an important role in many human–nature interactions, but these seasonal patterns are often overlooked in conservation. Here, we provide the first broad exploration of seasonal patterns of interest in nature across many species and cultures. Using data from Wikipedia, a large online encyclopedia, we analyzed 2.33 billion pageviews to articles for 31,751 species across 245 languages. We show that seasonality plays an important role in how and when people interact with plants and animals online. In total, over 25% of species in our data set exhibited a seasonal pattern in at least one of their language-edition pages, and seasonality is significantly more prevalent in pages for plants and animals than it is in a random selection of Wikipedia articles. Pageview seasonality varies across taxonomic clades in ways that reflect observable patterns in phenology, with groups such as insects and flowering plants having higher seasonality than mammals. Differences between Wikipedia language editions are significant; pages in languages spoken at higher latitudes exhibit greater seasonality overall, and species seldom show the same pattern across multiple language editions. These results have relevance to conservation policy formulation and to improving our understanding of what drives human interest in biodiversity.

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<![CDATA[Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited]]> https://www.researchpad.co/article/5c5df345d5eed0c484581069

The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches.

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<![CDATA[The effectiveness of using entertainment education narratives to promote safer sexual behaviors of youth: A meta-analysis, 1985-2017]]> https://www.researchpad.co/article/5c6c758ed5eed0c4843cfe95

Background

Risky sexual behaviors are associated with the transmission of sexually transmitted infections (STIs) and unwanted pregnancies, both major health concerns for youth worldwide. This review studies the effectiveness of narrated mass media programs in promoting safer sexual practices among youth in developed and developing countries.

Methods

Electronic and manual searches were conducted to identify experimental and quasi-experimental studies with robust counterfactual designs published between 1985 and the first quarter of 2017. Effect sizes were meta-analyzed using mixed-effects models.

Results

Eight experimental and two quasi-experimental studies met our inclusion criteria. The aggregated sample size was 23,476 participants, with a median of 902 participants per study. Entertainment education narratives had small but significant effects for three sexual behaviors. It reduced the number of sexual partners [standardized mean difference, (SMD) = 0.17, 95% confidence interval (CI) = 0.02–0.33, three effect sizes], reduced unprotected sex (SMD = 0.08, 95% CI = 0.03–0.12, nine effect sizes), and increased testing and management for STIs (SMD = 0.29, 95% CI = 0.11–0.46, two effect sizes). The interventions were not effective in reducing inter-generational sex, measured through the age-gap with sexual partners (SMD = 0.06, 95% CI = -0.06–0.19, four effect sizes). Entertainment education had medium-size effects on knowledge outcomes (SMD = 0.67, 95% CI = 0.32–1.02, seven effect sizes), where a time-decay relationship is observed. No effects were found on attitudes.

Conclusion

Although mass media entertainment had small effects in promoting safer sexual practices, its economies of scales over face-to-face interventions suggest its potential to be a cost-effective tool above an audience threshold. The use of study participants from the general youth population and the use of mostly effectiveness trials mitigate concerns regarding its scalability. The overall paucity of high-quality studies affirms the need for strengthening the evidence base of entertainment education. Future research should be undertaken to understand the moderator effects for different subgroups and intervention characteristics.

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<![CDATA[Social representation of palliative care in the Spanish printed media: A qualitative analysis]]> https://www.researchpad.co/article/5c57e65fd5eed0c484ef2e16

Background

Lack of social awareness is a major barrier to the development of palliative care. Mass media influences public opinion, and frequently deal with palliative care contributing to its image and public understanding.

Aim

To analyse how palliative care is portrayed in Spanish newspapers, as well as the contribution made by the press to its social representation.

Design

Based on criteria of scope and editorial plurality, four print newspapers were selected. Using the newspaper archive MyNews (www.mynews.es), articles published between 2009 and 2014 containing the words “palliative care” or “palliative medicine” were identified. Sociological discourse analysis was performed on the identified texts on two levels: a) contextual analysis, focusing on the message as a statement; b) interpretative analysis, considering the discourse as a social product.

Results

We examined 262 articles. Politician and healthcare professionals were the main representatives transmitting messages on palliative care. The discourses identified were characterised by: strong ideological and moral content focusing on social debate, strong ties linking palliative care and death and, to a lesser degree, as a healthcare service. The messages transmitted by representatives with direct experience in palliative care (professionals, patients and families) contributed the most to building a positive image of this healthcare practice. Overall, media reflect different interests in framing public understanding about palliative care.

Conclusion

The knowledge generated about how palliative care is reflected in the printed media may help to understand better one of the main barriers to its development not only in Spain, but also in other contexts.

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<![CDATA[Quick question or intensive inquiry: The role of message elaboration in the effectiveness of self-persuasive anti-alcohol posters]]> https://www.researchpad.co/article/5c536b25d5eed0c484a48179

Self-persuasion (i.e., generating your own arguments) is often more persuasive than direct persuasion (i.e., being provided with arguments), even when the technique is applied in media messages by framing the message as a question. It is unclear, however, if these messages are more persuasive when viewed for a long period to allow more elaboration about the message, or for a short period to reduce elaboration. In the current experiment, this is addressed by examining whether anti-alcohol posters framed as a statement (direct persuasion) or an open-ended question (self-persuasion) are more effective to reduce alcohol consumption under conditions of short- or long message exposure, compared to a control condition (no poster). Additionally, the potentially moderating roles of self-perceived alcohol identity and self-esteem on both types of persuasion are examined. Participants (N = 149) were exposed to a self-persuasion or direct persuasion anti-alcohol poster, either briefly before or continuously during a bogus beer taste task. The amount of alcohol consumed was the covert dependent variable. Contrary to expectations, both posters failed to affect alcohol consumption, regardless of exposure length. No moderation effects for self-perceived alcohol identity and self-esteem of the participants were found. Possible explanations are discussed.

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<![CDATA[Emergence of online communities: Empirical evidence and theory]]> https://www.researchpad.co/article/5bf5cbdfd5eed0c484a80a2a

Online communities, which have become an integral part of the day-to-day life of people and organizations, exhibit much diversity in both size and activity level; some communities grow to a massive scale and thrive, whereas others remain small, and even wither. In spite of the important role of these proliferating communities, there is limited empirical evidence that identifies the dominant factors underlying their dynamics. Using data collected from seven large online platforms, we observe a relationship between online community size and its activity which generally repeats itself across platforms: First, in most platforms, three distinct activity regimes exist—one of low-activity and two of high-activity. Further, we find a sharp activity phase transition at a critical community size that marks the shift between the first and the second regime in six out of the seven online platforms. Essentially, we argue that it is around this critical size that sustainable interactive communities emerge. The third activity regime occurs above a higher characteristic size in which community activity reaches and remains at a constant and higher level. We find that there is variance in the steepness of the slope of the second regime, that leads to the third regime of saturation, but that the third regime is exhibited in six of the seven online platforms. We propose that the sharp activity phase transition and the regime structure stem from the branching property of online interactions.

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<![CDATA[Checking facts and fighting back: Why journalists should defend their profession]]> https://www.researchpad.co/article/5c1813b5d5eed0c4847759e9

Bias accusations have eroded trust in journalism to impartially check facts. Traditionally journalists have avoided responding to such accusations, resulting in an imbalanced flow of arguments about the news media. This study tests what would happen if journalists spoke up more in defense of their profession, while simultaneously also testing effects of doing more fact checking. A five-day field experiment manipulated whether an online news portal included fact check stories and opinion pieces defending journalism. Fact checking was beneficial in terms of three democratically desirable outcomes–media trust, epistemic political efficacy, and future news use intent–only when defense of journalism stories were also present. No partisan differences were found in effects: Republicans, Democrats, and Independents were all affected alike. These results have important implications for journalistic practice as well as for theories and methods of news effects.

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<![CDATA[Predicting altcoin returns using social media]]> https://www.researchpad.co/article/5c1028cad5eed0c484248176

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called “altcoins” often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.

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<![CDATA[Social media popularity and election results: A study of the 2016 Taiwanese general election]]> https://www.researchpad.co/article/5c0841f0d5eed0c484fcb37b

This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election.

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<![CDATA[Caveat emptor, computational social science: Large-scale missing data in a widely-published Reddit corpus]]> https://www.researchpad.co/article/5b4a289a463d7e4513b8980b

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include “every publicly available Reddit comment” which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit.

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<![CDATA[Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data]]> https://www.researchpad.co/article/5989da08ab0ee8fa60b767e0

We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search data statistically outperform the competing models at all considered horizons. These results hold also with several robustness checks, considering alternative keywords, a falsification test, different out-of-samples, directional accuracy and forecasts at the state-level.

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<![CDATA[A productive clash of perspectives? The interplay between articles’ and authors’ perspectives and their impact on Wikipedia edits in a controversial domain]]> https://www.researchpad.co/article/5989db5cab0ee8fa60be02b0

This study examined predictors of the development of Wikipedia articles that deal with controversial issues. We chose a corpus of articles in the German-language version of Wikipedia about alternative medicine as a representative controversial issue. We extracted edits made until March 2013 and categorized them using a supervised machine learning setup as either being pro conventional medicine, pro alternative medicine, or neutral. Based on these categories, we established relevant variables, such as the perspectives of articles and of authors at certain points in time, the (im)balance of an article’s perspective, the number of non-neutral edits per article, the number of authors per article, authors’ heterogeneity per article, and incongruity between authors’ and articles’ perspectives. The underlying objective was to predict the development of articles’ perspectives with regard to the controversial topic. The empirical part of the study is embedded in theoretical considerations about editorial biases and the effectiveness of norms and rules in Wikipedia, such as the neutral point of view policy. Our findings revealed a selection bias where authors edited mainly articles with perspectives similar to their own viewpoint. Regression analyses showed that an author’s perspective as well as the article’s previous perspectives predicted the perspective of the resulting edits, albeit both predictors interact with each other. Further analyses indicated that articles with more non-neutral edits were altogether more balanced. We also found a positive effect of the number of authors and of the authors’ heterogeneity on articles’ balance. However, while the effect of the number of authors was reserved to pro-conventional medicine articles, the authors’ heterogenity effect was restricted to pro-alternative medicine articles. Finally, we found a negative effect of incongruity between authors’ and articles’ perspectives that was pronounced for the pro-alternative medicine articles.

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<![CDATA[The Role of Temporal Trends in Growing Networks]]> https://www.researchpad.co/article/5989da59ab0ee8fa60b8f739

The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment.

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<![CDATA[Can Simple Transmission Chains Foster Collective Intelligence in Binary-Choice Tasks?]]> https://www.researchpad.co/article/5989d9f5ab0ee8fa60b6fde6

In many social systems, groups of individuals can find remarkably efficient solutions to complex cognitive problems, sometimes even outperforming a single expert. The success of the group, however, crucially depends on how the judgments of the group members are aggregated to produce the collective answer. A large variety of such aggregation methods have been described in the literature, such as averaging the independent judgments, relying on the majority or setting up a group discussion. In the present work, we introduce a novel approach for aggregating judgments—the transmission chain—which has not yet been consistently evaluated in the context of collective intelligence. In a transmission chain, all group members have access to a unique collective solution and can improve it sequentially. Over repeated improvements, the collective solution that emerges reflects the judgments of every group members. We address the question of whether such a transmission chain can foster collective intelligence for binary-choice problems. In a series of numerical simulations, we explore the impact of various factors on the performance of the transmission chain, such as the group size, the model parameters, and the structure of the population. The performance of this method is compared to those of the majority rule and the confidence-weighted majority. Finally, we rely on two existing datasets of individuals performing a series of binary decisions to evaluate the expected performances of the three methods empirically. We find that the parameter space where the transmission chain has the best performance rarely appears in real datasets. We conclude that the transmission chain is best suited for other types of problems, such as those that have cumulative properties.

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<![CDATA[Identifying Topics in Microblogs Using Wikipedia]]> https://www.researchpad.co/article/5989da32ab0ee8fa60b84bd8

Twitter is an extremely high volume platform for user generated contributions regarding any topic. The wealth of content created at real-time in massive quantities calls for automated approaches to identify the topics of the contributions. Such topics can be utilized in numerous ways, such as public opinion mining, marketing, entertainment, and disaster management. Towards this end, approaches to relate single or partial posts to knowledge base items have been proposed. However, in microblogging systems like Twitter, topics emerge from the culmination of a large number of contributions. Therefore, identifying topics based on collections of posts, where individual posts contribute to some aspect of the greater topic is necessary. Models, such as Latent Dirichlet Allocation (LDA), propose algorithms for relating collections of posts to sets of keywords that represent underlying topics. In these approaches, figuring out what the specific topic(s) the keyword sets represent remains as a separate task. Another issue in topic detection is the scope, which is often limited to specific domain, such as health. This work proposes an approach for identifying domain-independent specific topics related to sets of posts. In this approach, individual posts are processed and then aggregated to identify key tokens, which are then mapped to specific topics. Wikipedia article titles are selected to represent topics, since they are up to date, user-generated, sophisticated articles that span topics of human interest. This paper describes the proposed approach, a prototype implementation, and a case study based on data gathered during the heavily contributed periods corresponding to the four US election debates in 2012. The manually evaluated results (0.96 precision) and other observations from the study are discussed in detail.

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<![CDATA[The Effects of Community Attachment and Information Seeking on Displaced Disaster Victims’ Decision Making]]> https://www.researchpad.co/article/5989da9eab0ee8fa60ba4b95

This paper uses original survey data of the Great East Japan earthquake disaster victims to examine their decision to apply for the temporary housing as well as the timing of application. We assess the effects of victims’ attachment to their locality as well as variation in victims’ information seeking behavior. We additionally consider various factors such as income, age, employment and family structure that are generally considered to affect the decision to choose temporary housing as victims’ solution for their displacement. Empirical results indicate that, ceteris paribus, as the degree of attachment increases, victims are more likely to apply for the temporary housing but attachment does not affect the timing of application. On the other hand, the victims who actively seek information and are able to collect higher quality information are less likely to apply for the temporary housing and if they do apply then they apply relatively later.

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<![CDATA[Leveraging Big Data for Exploring Occupational Diseases-Related Interest at the Level of Scientific Community, Media Coverage and Novel Data Streams: The Example of Silicosis as a Pilot Study]]> https://www.researchpad.co/article/5989daa6ab0ee8fa60ba77e7

Objective

Silicosis is an untreatable but preventable occupational disease, caused by exposure to silica. It can progressively evolve to lung impairment, respiratory failure and death, even after exposure has ceased. However, little is known about occupational diseases-related interest at the level of scientific community, media coverage and web behavior. This article aims at filling in this gap of knowledge, taking the silicosis as a case study.

Methods

We investigated silicosis-related web-activities using Google Trends (GT) for capturing the Internet behavior worldwide in the years 2004–2015. GT-generated data were, then, compared with the silicosis-related scientific production (i.e., PubMed and Google Scholar), the media coverage (i.e., Google news), the Wikipedia traffic (i.e, Wikitrends) and the usage of new media (i.e., YouTube and Twitter).

Results

A peak in silicosis-related web searches was noticed in 2010–2011: interestingly, both scientific articles production and media coverage markedly increased after these years in a statistically significant way. The public interest and the level of the public engagement were witnessed by an increase in likes, comments, hashtags, and re-tweets. However, it was found that only a small fraction of the posted/uploaded material contained accurate scientific information.

Conclusions

GT could be useful to assess the reaction of the public and the level of public engagement both to novel risk-factors associated to occupational diseases, and possibly related changes in disease natural history, and to the effectiveness of preventive workplace practices and legislative measures adopted to improve occupational health. Further, occupational clinicians should become aware of the topics most frequently searched by patients and proactively address these concerns during the medical examination. Institutional bodies and organisms should be more present and active in digital tools and media to disseminate and communicate scientifically accurate information. This manuscript should be intended as preliminary, exploratory communication, paving the way for further studies.

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<![CDATA[Even good bots fight: The case of Wikipedia]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbb37

In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits over the period 2001–2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these sterile “fights” may sometimes continue for years. Unlike humans on Wikipedia, bots’ interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively “dumb” bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.

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