Social Structure and Opinion Formation

We present a dynamical theory of opinion formation that takes explicitly into account the structure of the social network in which in- dividuals are embedded. The theory predicts the evolution of a set of opinions through the social network and establishes the existence of a martingale property, i.e. that the expected weighted fraction of the population that holds a given opinion is constant in time. Most importantly, this weighted fraction is not either zero or one, but corresponds to a non-trivial distribution of opinions in the long time limit. This co-existence of opinions within a social network is in agreement with the often observed locality effect, in which an opinion or a fad is localized to given groups without infecting the whole society. We verified these predictions, as well as those concerning the fragility of opinions and the importance of highly connected individuals in opinion formation, by performing computer experiments on a number of social networks

What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation

Recent discussions of annotator agreement have mostly centered around its calculation and interpretation, and the correct choice of indices. Although these discussions are important, they only consider the "back-end" of the story, namely, what to do once the data are collected. Just as important in our opinion is to know how agreement is reached in the first place and what factors influence coder agreement as part of the annotation process or setting, as this knowledge can provide concrete guidelines for the planning and set-up of annotation projects. To investigate whether there are factors that consistently impact annotator agreement we conducted a meta-analytic investigation of annotation studies reporting agreement percentages. Our meta-analysis synthesized factors reported in 96 annotation studies from three domains (word-sense disambiguation, prosodic transcriptions, and phonetic transcriptions) and was based on a total of 346 agreement indices. Our analysis identified seven factors that influence reported agreement values: annotation domain, number of categories in a coding scheme, number of annotators in a project, whether annotators received training, the intensity of annotator training, the annotation purpose, and the method used for the calculation of percentage agreements. Based on our results we develop practical recommendations for the assessment, interpretation, calculation, and reporting of coder agreement. We also briefly discuss theoretical implications for the concept of annotation quality

Decision support system to help choose between an ITE or a BTE hearing aid

A decision support system (DSS) is used for analysing a situation and making decisions. The goal of this research is to mine a large set of heterogeneous audiology data and create a DSS to help audiology technicians to choose between an ITE or BTE hearing aid. Although, in many cases such a choice is clear cut, but at other times this system could be used as a second opinion to predict the hearing aid type. A number of data mining techniques, such as clustering of audiograms, association analysis of variables (such as, age, gender, diagnosis, masker, mould and free text keywords) using contingency tables and principal component analysis on audiograms were used to find candidate variables to be combined into a DSS. The DSS was created using the techniques of logistic regression, Naïve Bayesian analysis and Bayesian networks, and these systems were tested and validated on test data to see which of the techniques produced the better results. This DSS takes air and bone conduction frequencies, age, gender, diagnosis, masker, mould and some free text words associated with a patient as input and gives as the output a decision as to whether the patient would be more likely to prefer an ITE or a BTE hearing aid type. The highest agreement between predicted results and actual hearing aid type in the data were obtained using Bayesian networks, with 93 to 94 percent similarity overall, with a precision of 0.91 for ITE and 0.96 for BTE. The reason for this might be that the Bayesian network also considers interaction between variables while the other two techniques (logistic regression and Naïve Bayesian analysis) consider only the individual variables. One of the important features of this DSS is that once the final choice of hearing aid type is predicted, the decision process can be tracked back to see which factors (variables) contributed how much to the final decision. The theoretical upper bound of classifier performance is the inter-annotator agreement (Altman, 1991), in this case the rate at which two expert audiologists would assign the same hearing aid to the same patient. Unfortunately, this type of data was not included in the audiology database

Examining the Impact of a Reasoning Aid to Help People Evaluate the Evidentiary Weight of Consensus

This item is only available electronically.Social media is a vortex of information and people may see distorted views of consensus, where the independence of information and sources is unclear. A tool that summarises consensus information might help people to navigate these important cues. This study examined whether a reasoning aid (in the form of a diagram) visually illustrating both the number of independent people supporting/disagreeing with a claim and the diversity of arguments would persuade people to change their original beliefs. Participants (n=605) were recruited through Amazon’s Mechanical Turk to evaluate 24 claims on a mock Twitter interface. Participants were randomly assigned to conditions with either tweets only, diagram only or tweets with a diagram. Participants rated their initial agreement level (0-100) with each claim and then saw the diagram and/or set of tweets, then were able to update their agreement level if their original opinion had now changed. The findings of this study show that without assistance, people mostly rely on cues of argument quantity, such as the number of tweets for a given stance. However, when presented with a diagram, people were able to utilise cues of argument quality, such as when there were different sources providing the information and when multiple arguments were used.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

An Italian expert consensus on the use of opioids for the management of chronic non-oncological pain in clinical practice: focus on buprenorphine

Purpose: The aim of the present work was to evaluate the knowledge and prescriptive habits of clinicians involved in the management of chronic non cancer pain (CNCP), with a special focus on the use of opioids. Methods: A Delphi method was used. A Board of specialists elaborated and discussed a series of statements, based on available literature and personal clinical expertise, about particularly controversial topics on pain pathophysiology and treatment. A Panel of experts in the field of pain management, selected by the Board, was invited to vote the proposed statements, indicating the level of agreement on a 5-point Likert scale (1: strongly disagree; 2: disagree; 3: partially agree; 4: agree; 5: strongly agree). The threshold for consensus was set at minimum 66.6% of the number of respondents with a level of agreement ≥4 (Agree or Strongly agree). Results: The Board included 5 pain therapists, 1 pharmacologist and 1 methodology expert and drew up a total of 36 statements (for a total of 40 requested answers)”. A total of 100 clinicians were included in the Expert Panel. Respondents were 89 (89%). Consensus was achieved for 32 out of 40 answers. Most of the lack of consensus was recorded for statements regarding opioids use, and resulted from a low level of agreement (3 on the Likert scale), suggesting a neutral position deriving from a lack of knowledge rather than a strong contrary opinion. Conclusion: Most of the proposed items reached consensus, suggesting a generally homogeneous approach to CNCP management. However, the lack of consensus recorded for several items regarding opioid use confirms the need to fill important gaps in the knowledge of available agents. A clear explanation of the peculiar pharmacological properties of drugs associated with potential clinical advantages (such as buprenorphine) will help optimize pain treatment in both primary care and hospital settings and improving pain control in CNCP patients

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

A Fuzzy Delphi Consensus Methodology Based on a Fuzzy Ranking

Delphi multi-round survey is a procedure that has been widely and successfully used to aggregate experts’ opinions about some previously established statements or questions. Such opinions are usually expressed as real numbers and some commentaries. The evolution of the consensus can be shown by an increase in the agreement percentages, and a decrease in the number of comments made. A consensus is reached when this percentage exceeds a certain previously set threshold. If this threshold has not been reached, the moderator modifies the questionnaire according to the comments he/she has collected, and the following round begins. In this paper, a new fuzzy Delphi method is introduced. On the one hand, the experts’ subjective judgments are collected as fuzzy numbers, enriching the approach. On the other hand, such opinions are collected through a computerized application that is able to interpret the experts’ opinions as fuzzy numbers. Finally, we employ a recently introduced fuzzy ranking methodology, satisfying many properties according to human intuition, in order to determine whether the expert’s fuzzy opinion is favorable enough (comparing with a fixed fuzzy number that indicates Agree or Strongly Agree). A cross-cultural validation was performed to illustrate the applicability of the proposed method. The proposed approach is simple for two reasons: it does not need a defuzzification step of the experts’ answers, and it can consider a wide range of fuzzy numbers not only triangular or trapezoidal fuzzy numbers

Development of an internationally agreed minimal dataset for juvenile dermatomyositis (JDM) for clinical and research use

Background: Juvenile dermatomyositis (JDM) is a rare autoimmune inflammatory disorder associated with significant morbidity and mortality. International collaboration is necessary to better understand the pathogenesis of the disease, response to treatment and long-term outcome. To aid international collaboration, it is essential to have a core set of data that all researchers and clinicians collect in a standardised way for clinical purposes and for research. This should include demographic details, diagnostic data and measures of disease activity, investigations and treatment. Variables in existing clinical registries have been compared to produce a provisional data set for JDM. We now aim to develop this into a consensus-approved minimum core dataset, tested in a wider setting, with the objective of achieving international agreement. Methods/Design: A two-stage bespoke Delphi-process will engage the opinion of a large number of key stakeholders through Email distribution via established international paediatric rheumatology and myositis organisations. This, together with a formalised patient/parent participation process will help inform a consensus meeting of international experts that will utilise a nominal group technique (NGT). The resulting proposed minimal dataset will be tested for feasibility within existing database infrastructures. The developed minimal dataset will be sent to all internationally representative collaborators for final comment. The participants of the expert consensus group will be asked to draw together these comments, ratify and 'sign off' the final minimal dataset. Discussion: An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. The final approved minimum core dataset could be rapidly incorporated into national and international collaborative efforts, including existing prospective databases, and be available for use in randomised controlled trials and for treatment/protocol comparisons in cohort studies