A multi-disciplinary perspective on emergent and future innovations in peer review [version 2; referees: 2 approved]

Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform and reduce the biases of existing models as much as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that could, at least partially, resolve many of the socio-technical issues associated with peer review, and potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments

Twitter language use reflects psychological differences between Democrats and Republicans

Previous research has shown that political leanings correlate with various psychological factors. While surveys and experiments provide a rich source of information for political psychology, data from social networks can offer more naturalistic and robust material for analysis. This research investigates psychological differences between individuals of different political orientations on a social networking platform, Twitter. Based on previous findings, we hypothesized that the language used by liberals emphasizes their perception of uniqueness, contains more swear words, more anxiety-related words and more feeling-related words than conservatives' language. Conversely, we predicted that the language of conservatives emphasizes group membership, contains more certainty and more references to achievement and religion than liberals' language. We analysed Twitter timelines of 5,373 followers of three Twitter accounts of the American Democratic and 5,386 followers of three accounts of the Republican parties' Congressional Organizations. The results support most of the predictions and previous findings, confirming that Twitter behaviour offers valid insights to offline behaviour

A multi-disciplinary perspective on emergent and future innovations in peer review

Peer review of research articles is a core part of our scholarly communication system. In spite of its importance, the status and purpose of peer review is often contested. What is its role in our modern digital research and communications infrastructure? Does it perform to the high standards with which it is generally regarded? Studies of peer review have shown that it is prone to bias and abuse in numerous dimensions, frequently unreliable, and can fail to detect even fraudulent research. With the advent of web technologies, we are now witnessing a phase of innovation and experimentation in our approaches to peer review. These developments prompted us to examine emerging models of peer review from a range of disciplines and venues, and to ask how they might address some of the issues with our current systems of peer review. We examine the functionality of a range of social Web platforms, and compare these with the traits underlying a viable peer review system: quality control, quantified performance metrics as engagement incentives, and certification and reputation. Ideally, any new systems will demonstrate that they out-perform and reduce the biases of existing models as much as possible. We conclude that there is considerable scope for new peer review initiatives to be developed, each with their own potential issues and advantages. We also propose a novel hybrid platform model that could, at least partially, resolve many of the socio-technical issues associated with peer review, and potentially disrupt the entire scholarly communication system. Success for any such development relies on reaching a critical threshold of research community engagement with both the process and the platform, and therefore cannot be achieved without a significant change of incentives in research environments

An empirical test of partner choice mechanisms in a wild legume–rhizobium interaction

Mutualisms can be viewed as biological markets in which partners of different species exchange goods and services to their mutual benefit. Trade between partners with conflicting interests requires mechanisms to prevent exploitation. Partner choice theory proposes that individuals might foil exploiters by preferentially directing benefits to cooperative partners. Here, we test this theory in a wild legume–rhizobium symbiosis. Rhizobial bacteria inhabit legume root nodules and convert atmospheric dinitrogen (N(2)) to a plant available form in exchange for photosynthates. Biological market theory suits this interaction because individual plants exchange resources with multiple rhizobia. Several authors have argued that microbial cooperation could be maintained if plants preferentially allocated resources to nodules harbouring cooperative rhizobial strains. It is well known that crop legumes nodulate non-fixing rhizobia, but allocate few resources to those nodules. However, this hypothesis has not been tested in wild legumes which encounter partners exhibiting natural, continuous variation in symbiotic benefit. Our greenhouse experiment with a wild legume, Lupinus arboreus, showed that although plants frequently hosted less cooperative strains, the nodules occupied by these strains were smaller. Our survey of wild-grown plants showed that larger nodules house more Bradyrhizobia, indicating that plants may prevent the spread of exploitation by favouring better cooperators

An empirical test of partner choice mechanisms in a wild legume-rhizobium interaction

Mutualisms can be viewed as biological markets in which partners of different species exchange goods and services to their mutual benefit. Trade between partners with conflicting interests requires mechanisms to prevent exploitation. Partner choice theory proposes that individuals might foil exploiters by preferentially directing benefits to cooperative partners. Here, we test this theory in a wild legume-rhizobium symbiosis. Rhizobial bacteria inhabit legume root nodules and convert atmospheric dinitrogen (N-2) to a plant available form in exchange for photosynthates. Biological market theory suits this interaction because individual plants exchange resources with multiple rhizobia. Several authors have argued that microbial cooperation could be maintained if plants preferentially allocated resources to nodules harbouring cooperative rhizobial strains. It is well known that crop legumes nodulate non-fixing rhizobia, but allocate few resources to those nodules. However, this hypothesis has not been tested in wild legumes which encounter partners exhibiting natural, continuous variation in symbiotic benefit. Our greenhouse experiment with a wild legume, Lupinus arboreus, showed that although plants frequently hosted less cooperative strains, the nodules occupied by these strains were smaller. Our survey of wild-grown plants showed that larger nodules house more Bradyrhizobia, indicating that plants may prevent the spread of exploitation by favouring better cooperators

A Classification Framework for Online Social Support using Deep Learning

Health consumers engage in social interactions in online health communities (OHCs) to seek or provide social support. Automatic classification of social support exchanged online is important for both researchers and practitioners of online health communities, especially when a large number of messages are posted on regular basis. Classification of social support in OHCs provides an efficient way to assess the effectiveness of social interactions in the virtual environment. Most previous studies of online social support classification are based on bag-of-words assumption and have not considered the semantic meaning of words/terms embedded in the online messages. This research proposes a classification framework for online social support using the recent development of word space models and deep learning methods. Specifically, doc2vec models, bag-of-words representations, and linguistic analysis methods are used to extract features from the text messages that are posted in OHC for online social interaction or social support exchange. Then a deep learning model is applied to classify two major types of social support (i.e., informational and emotional support) expressed in OHC reply messages