In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google, 2019). Also, governmental authorities started acting against organisations that show to have a high number of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals, it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the reputation of an organisation and lead to a disvalued source to determine the public opinion about brands. Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied for a while now, there are only a limited number of spam detection models available for companies to protect their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on online presence and limit the amount of negative input that affects their competitive position which can even lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam detector based on supervised machine learning is derived in an experiment that performs quite well on the well-known Amazon Mechanical Turk dataset.
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This contribution is based on a transcript of the podcast series Voiceblind, by Morgane Billuart at ARIAS during covid lockdowns. In preparation, we - Carlo De Gaetano, Andy Dockett and Sabine Niederer of the Visual Methodologies Collective- were provided with a list of questions and a piece of hardware (a zoom recorder) to interview ourselves about our research practice. To meet in person and at a safe distance, we set out to Westerpark and found a spot where we set up the microphone and held our conversation - to be at times interrupted by an inquisitive duck or a train passing by. We talked for over an hour, and then Andy edited the conversation down to a little under 15 minutes, to fit the format of the podcast series. We inserted a reading of a story by Janine Armin, editor and narrator of Turning to the Birds, our series of machine co-authored climate fiction stories. And lastly, Morgane added her introduction before it was launched by ARIAS as the 5th episode in the Voiceblind podcast series.
Background To gain insight into the role of plantar intrinsic foot muscles in fall-related gait parameters in older adults, it is fundamental to assess foot muscles separately. Ultrasonography is considered a promising instrument to quantify the strength capacity of individual muscles by assessing their morphology. The main goal of this study was to investigate the intra-assessor reliability and measurement error for ultrasound measures for the morphology of selected foot muscles and the plantar fascia in older adults using a tablet-based device. The secondary aim was to compare the measurement error between older and younger adults and between two different ultrasound machines. Methods Ultrasound images of selected foot muscles and the plantar fascia were collected in younger and older adults by a single operator, intensively trained in scanning the foot muscles, on two occasions, 1–8 days apart, using a tablet-based and a mainframe system. The intra-assessor reliability and standard error of measurement for the cross-sectional area and/or thickness were assessed by analysis of variance. The error variance was statistically compared across age groups and machines. Results Eighteen physically active older adults (mean age 73.8 (SD: 4.9) years) and ten younger adults (mean age 21.9 (SD: 1.8) years) participated in the study. In older adults, the standard error of measurement ranged from 2.8 to 11.9%. The ICC ranged from 0.57 to 0.97, but was excellent in most cases. The error variance for six morphology measures was statistically smaller in younger adults, but was small in older adults as well. When different error variances were observed across machines, overall, the tablet-based device showed superior repeatability. Conclusions This intra-assessor reliability study showed that a tablet-based ultrasound machine can be reliably used to assess the morphology of selected foot muscles in older adults, with the exception of plantar fascia thickness. Although the measurement errors were sometimes smaller in younger adults, they seem adequate in older adults to detect group mean hypertrophy as a response to training. A tablet-based ultrasound device seems to be a reliable alternative to a mainframe system. This advocates its use when foot muscle morphology in older adults is of interest.
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