Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree.

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy.

Association of asthma exacerbations with paper mulberry (Broussenetia papyrifera) pollen in Islamabad: An observational study.

Background: Although the role of airborne plant pollen in causing allergic rhinitis has been established, the association of concentrations of paper mulberry (Broussenetia papyrifera) pollens in the air and incidence of asthma exacerbations has not, despite an observed increase in the number of asthma patients attending physician clinics and hospital Accident and Emergency (A&E) Departments during the paper mulberry pollen season. We aimed to assess the association between paper mulberry pollen concentrations (typically peaking in March each year) and asthma exacerbations in the city of Islamabad. Methods: We used three approaches to investigate the correlation of paper mulberry pollen concentration with asthma exacerbations: A retrospective analysis of historical records (2000-2019) of asthma exacerbations of patients from the Allergy and Asthma Institute, Pakistan (n = 284), an analysis of daily nebulisations in patients attending the A&E Department of the Pakistan Institute of Medical Sciences (March 2020 to July 2021), a prospective peak expiratory flow rate (PEFR) diary from participants (n = 40) with or without asthma and with or without paper mulberry sensitisation. We examined associations between pollen data and asthma exacerbations using Pearson correlation. Results: We found a strong positive correlation between mean paper mulberry pollen counts and clinical records of asthma exacerbations in patients sensitised to paper mulberry (Pearson correlation coefficient (r) = 0.86; P < 0.001), but not in non-sensitised patients (r = 0.32; P = 0.3). There was a moderate positive correlation between monthly nebulisation counts and pollen counts (r = 0.56; P = 0.03), and a strong negative correlation between percent predicted PEFR and pollen counts in sensitised asthma patients (r = -0.72, P < 0.001). However, these correlations were of low magnitude in the non-sensitised asthma (r = -0.16; P < 0.001) and sensitised non-asthma (r = -0.28; P < 0.001) groups. Conclusions: Our three approaches to analysis all showed an association between high paper mulberry pollen concentration in Islamabad and asthma exacerbations. Predicting pollen peaks could enable alerts and mobilise strategies to proactively manage these peaks of asthma exacerbations.

The (Mis)Information Game: A social media simulator.

Given the potential negative impact reliance on misinformation can have, substantial effort has gone into understanding the factors that influence misinformation belief and propagation. However, despite the rise of social media often being cited as a fundamental driver of misinformation exposure and false beliefs, how people process misinformation on social media platforms has been under-investigated. This is partially due to a lack of adaptable and ecologically valid social media testing paradigms, resulting in an over-reliance on survey software and questionnaire-based measures. To provide researchers with a flexible tool to investigate the processing and sharing of misinformation on social media, this paper presents The Misinformation Game-an easily adaptable, open-source online testing platform that simulates key characteristics of social media. Researchers can customize posts (e.g., headlines, images), source information (e.g., handles, avatars, credibility), and engagement information (e.g., a post's number of likes and dislikes). The platform allows a range of response options for participants (like, share, dislike, flag) and supports comments. The simulator can also present posts on individual pages or in a scrollable feed, and can provide customized dynamic feedback to participants via changes to their follower count and credibility score, based on how they interact with each post. Notably, no specific programming skills are required to create studies using the simulator. Here, we outline the key features of the simulator and provide a non-technical guide for use by researchers. We also present results from two validation studies. All the source code and instructions are freely available online at https://misinfogame.com .

Promoting and countering misinformation during Australia's 2019-2020 bushfires: a case study of polarisation.

During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson surfaced on Twitter using #ArsonEmergency. The extent to which bots and trolls were responsible for disseminating and amplifying this misinformation has received media scrutiny and academic research. Here, we study Twitter communities spreading this misinformation during the newsworthy event, and investigate the role of online communities using a natural experiment approach-before and after reporting of bots promoting the hashtag was broadcast by the mainstream media. Few bots were found, but the most bot-like accounts were social bots, which present as genuine humans, and trolling behaviour was evident. Further, we distilled meaningful quantitative differences between two polarised communities in the Twitter discussion, resulting in the following insights. First, Supporters of the arson narrative promoted misinformation by engaging others directly with replies and mentions using hashtags and links to external sources. In response, Opposers retweeted fact-based articles and official information. Second, Supporters were embedded throughout their interaction networks, but Opposers obtained high centrality more efficiently despite their peripheral positions. By the last phase, Opposers and unaffiliated accounts appeared to coordinate, potentially reaching a broader audience. Finally, the introduction of the bot report changed the discussion dynamic: Opposers only responded immediately, while Supporters countered strongly for days, but new unaffiliated accounts drawn into the discussion shifted the dominant narrative from arson misinformation to factual and official information. This foiled Supporters' efforts, highlighting the value of exposing misinformation. We speculate that the communication strategies observed here could inform counter-strategies in other misinformation-related discussions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13278-022-00892-x.

Exploring the effect of streamed social media data variations on social network analysis.

To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

Exploiting contextual information to improve call prediction.

With the increase in contact list size of mobile phone users, the management and retrieval of contacts has becomes a tedious job. In this study, we analysed some important dimensions that can effectively contribute in predicting which contact a user is going to call at time t. We improved a state of the art algorithm, that uses frequency and recency by adding temporal information as an additional dimension for predicting future calls. The proposed algorithm performs better in overall analysis, but more significantly there was an improvement in the prediction of top contacts of a user as compared to the base algorithm.

An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.