A customisable pipeline for continuously harvesting socially-minded Twitter users

On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.Comment: Procs. ICWE 2019, June 2019, Kore

Social determinants of content selection in the age of (mis)information

Despite the enthusiastic rhetoric about the so called \emph{collective intelligence}, conspiracy theories -- e.g. global warming induced by chemtrails or the link between vaccines and autism -- find on the Web a natural medium for their dissemination. Users preferentially consume information according to their system of beliefs and the strife within users of opposite narratives may result in heated debates. In this work we provide a genuine example of information consumption from a sample of 1.2 million of Facebook Italian users. We show by means of a thorough quantitative analysis that information supporting different worldviews -- i.e. scientific and conspiracist news -- are consumed in a comparable way by their respective users. Moreover, we measure the effect of the exposure to 4709 evidently false information (satirical version of conspiracy theses) and to 4502 debunking memes (information aiming at contrasting unsubstantiated rumors) of the most polarized users of conspiracy claims. We find that either contrasting or teasing consumers of conspiracy narratives increases their probability to interact again with unsubstantiated rumors.Comment: misinformation, collective narratives, crowd dynamics, information spreadin

Glycotope structures and intramolecular affinity factors of plant lectins for Tn/T antigens

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Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA

Upregulated complement receptors correlate with Fc gamma receptor 3A-positive natural killer and natural killer-T cells in neuromyelitis optica spectrum disorder

BACKGROUND AND OBJECTIVES: Inhibition of terminal complement in neuromyelitis optica spectrum disorder (NMOSD) using eculizumab helps prevent relapses, but the exact mechanism of action of the drug remains unclear. Similarly, genetic variants in the Fc Gamma receptor 3A (FCGR3A), also known as CD16, are correlated with outcomes in NMOSD, but the immune cells expressing those CD16 are unknown. We compared CD16 expression on immune cells modulated by complement activity in natural killer (NK) cells and natural killer-T (NKT) cells in NMOSD to disease and normal-healthy controls. METHODS: Peripheral blood cell (PBMC) samples from 45 patients with NMOSD with aquaporin 4 (AQP4)-IgG, 18 disease controls, and 19 normal controls were analyzed for CD16 expression and complement receptors in vitro. RESULTS: At baseline, the number of NKT cells was increased in NMOSD (p < 0.001), but the proportion that was CD16 positive was lower compared to normal and disease controls (p = 0.0012). NK cell count was normal, but the ratio that was CD16 positive was also significantly lower (p < 0.001). In both NK cells and NKT cells from NMOSD, C5 complement receptor expression was much higher than normal and disease controls (p < 0.001 for both). We also evaluated activation markers CD69 and CD83, which were also significantly higher in NK and NKT cells from NMOSD patients. FCGR3A p158 V/V genotype group in NMOSD patients showed decreased NK cell proportion with activation, and fewer CD16-expressing NKT cells than the F/F genotype group. DISCUSSION: Our results support an immunopathogenesis model in which complement pathway activation in NK/NKT cells upregulates CD16 expression that binds to antibody/antigen complexes. In the context of NMOSD, these complement-sensitive cells may be responsible for the escalating autoimmune activity

Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

\u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting