Algorithmic Prosumers

Today almost everything we do in our everyday life is datafied and fed into an algorithm, i.e. reduced to an input that recursive computational systems process and transform into behavioural models. How algorithms sort, classify and propose contents have a striking impact on how people make sense of the world and derive their sense of self. Despite their powerful social presence, however, algorithms remain mainly invisible to individuals, as well as difficult to examine for researchers. By drawing on auto-ethnographic diaries, prepared following a critical pedagogy approach, this contribution discusses the results of an empirical research that aim to analyse media consumption, content production and sharing practices on digital platforms, in order to shed light on how individuals relate to algorithmic media and how they critically reflect on their apparently innocuous daily online practices. In accordance with the results, we argue that users on digital platforms can be framed as algorithmic prosumers. Indeed, the consumption, as well as the production of contents on digital platforms are algorithmic practices that foster datafication and capitalist surveillance logics, with users feeding algorithmic media while they are contemporarily fed by them within a recursive loop. In this context, it emerges an individual whose subjectivity is strictly connected to and enacted by computational procedures

Smart working is not so smart: Always-on lives and the dark side of platformisation

This article investigates the lived experiences of remote workers during the Italian lockdown, and the role of digital platforms in their working and everyday life activities, as well as the consequences of home confinement measures on personal and working conditions. Drawing on 20 in-depth semi-structured interviews, this paper support that, following a massive extension of transmedia work, remote workers experienced a ‘fractured’ and ‘always on’ life. During the lockdown, the even more pervasive role of digital media favoured the convergence of different spaces and times into the home, the erosion of the distinction between private and professional life and the exacerbation of previous social inequalities, especially gender and digital inequalities. In this scenario, platform and surveillance capitalist logics were further reinforced, while the presence bleed in the experiences of workers increased

Collect and Handle Personal Social Media Data. Ethical Issues of an Empirical Internet Research

Online users’ digital traces provide valuable information and empirical evidence, but Internet research requires scientific rigor in accessing and managing User Generated Contents (UGCs). The article challenges these practices and advocates for a reflexive approach to social media research ethics. Although platforms offer viable access, utilizing such data can intrude on subjects’ private lives. Defining responsibilities toward data and subjects is crucial when studying online contents, such as Instagram stories and Facebook posts. The subject’s centrality and ethical implications becomes particularly significant in social inquiry, where the object is closely tied to actively signifying subjects and social relations mediated by institutions or technologies. The paper explores ethical issues in a concrete research project, “7 friends for 7 days”, and presents alternative research practices for observing and analyzing online content within the post-API research context. It discusses ethical challenges in Internet research, focusing on social media data, and examines a study that analyzed user-generated content through human-type coding. The paper reflects on the ethical considerations in fabricating research evidence, particularly regarding UGC published on personal social media platforms and the critical awareness of those involved in observing and disseminating such data

Framing pandemic news. Empirical research on Covid-19 representation in the Italian TV news

The article contributes to the vast literature on the media representation of Covid-19, by exposing the results of a quantitative and qualitative analysis of Covid-19 media coverage in Italy, run on the full archive of prime-time TV news – Tg1, Tg2, Tg3, Tg4, Tg5, Studio Aperto, Tg La7 – between February 28, 2020, and February 27, 2021. All verbal contents of TV news have been analyzed, based on a sample of 2,555 news shows and 14,304 news stories related to the epidemic, for a total of 1.6 million words. By applying the media framing models, we realized a two-step work: a mapping of TV coverage across one year; and an in-depth investigation on the most relevant keywords, gathering and variant. The main results show how the different Italian television news broadcast the pandemic "waves", paying attention to issues considered as emergencies. Through a cluster analysis, we found some recurring and absent narratives of the media representation on Covid19. Along with the reflection on the framing of the pandemic, we will come out with some insights into the blaming strategies put in motion by the TV news

Socio-Narrative Representations of Immigrants by Italian Young People

This paper aims to explore how young Italians represent the phenomenon of migration by using a socio-narrative approach and taking into account the role of media in shaping the collective imaginary. To this end, socio-narrative representations are deemed a valuable tool both at the conceptual and analytical level. Accordingly, this empirical study relies on the three main dimensions of socio-narrative representations (objectification, anchoring and narration) and on two mass communication theories (agenda-setting and cultivation theory) to analyse qualitative data. In-depth semi-structured interviews revealed that the iconic dimension has a predominant role in the mythopoietic mechanisms of construction of socio-narrative presentations. In fact, it emerges that also the anchoring and narrative processes frequently originate from certain stereotypical pictures and stories continuously broadcasted by mass media, which select and portray only a partial and inaccurate depiction of migration, based on the narrative distinction between “us” and “them”. In fact, this complex phenomenon is often reduced to the images of the victims of humanitarian crises, thereby favouring processes of distant suffering and compassion fatigue. Implications of these findings are discussed, as well as suggestions for future research

Memes as socio-narrative representations of COVID-19. Themes, protagonists, and narratives of the pandemic memes in Italy

During the first year of the COVID-19 pandemic, memes appeared as one of the cultural artefacts through which people could represent, and ironize on, the consequences of the spread of the virus. This paper presents the results of a research, based on a content analysis conducted on a sample of 1882 memes, which investigate the main themes and protagonists of the memes and the following collective narratives built and circulated in three different phases of the pandemic, in the Italian socio-cultural context. Drawing on the findings of this exploratory study, we argue that memes are socio-cultural and narrative artifacts that contribute to construct, and in which are inscribed, socio-narrative representations, that individuals employ to make sense of, and build imaginaries and narratives about collective experiences. Specifically, we support that memes, as socio-narrative representations, objectify facts through iconic representations, anchor new events to previously elaborated ideas and expressions, and entail skeleton stories, i.e., narrative programs in which are inscribed normative and ethical elements. Key actors providing sources for meme repertoires and thus contributing to the construction of socio-narrative representations are broadcast media (news media, cinema, TV programs, etc.), which had a key role during the pandemic, following the social distancing and home confinement restrictions issued by the Italian government. During the hardest moments of the pandemic, memes worked as socio-narrative representations of the new type of everyday life with which individuals had to cope, thereby making familiar something that appeared dangerous and unknown. Memes eliminated the most disturbing and dangerous parts of the virus through narrations that often made fun of the new rules of behaviour and relationship practices, with ordinary objects and guiding characters, such as politicians and stars from the show business realm, that were able to reassure people. Finally, methodological insights regarding the study of memes at the big data & computational methods level are provided

Fall Detection with Event-Based Data:A Case Study

Fall detection systems are relevant in our aging society aiming to support efforts towards reducing the impact of accidental falls. However, current solutions lack the ability to combine low-power consumption, privacy protection, low latency response, and low payload. In this work, we address this gap through a comparative analysis of the trade-off between effectiveness and energy consumption by comparing a Recurrent Spiking Neural Network (RSNN) with a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). By leveraging two pre-existing RGB datasets and an event-camera simulator, we generated event data by converting intensity frames into event streams. Thus, we could harness the salient features of event-based data and analyze their benefits when combined with RSNNs and LSTMs. The compared approaches are evaluated on two data sets collected from a single subject; one from a camera attached to the neck (N-data) and the other one attached to the waist (W-data). Each data set contains 469 video samples, of which 213 are four types of fall examples, and the rest are nine types of non-fall daily activities. Compared to the CNN, which operates on the high-resolution RGB frames, the RSNN requires 200 × less trainable parameters. However, the CNN outperforms the RSNN by 23.7 and 17.1% points for W- and N-data, respectively. Compared to the LSTM, which operates on event-based input, the RSNN requires 5 × less trainable parameters and 2000 × less MAC operations while exhibiting a 1.9 and 8.7% points decrease in accuracy for W- and N-data, respectively. Overall, our results show that the event-based data preserves enough information to detect falls. Our work paves the way to the realization of high-energy efficient fall detection systems.</p

Very-Low-Calorie Ketogenic Diets with Whey, Vegetable or Animal Protein in Patients with Obesity: A Randomized Pilot Study

Context We compared the efficacy, safety and effect of 45-day isocaloric very-low-calorie ketogenic diets (VLCKDs) incorporating whey, vegetable or animal protein on the microbiota in patients with obesity and insulin resistance to test the hypothesis that protein source may modulate the response to VLCKD interventions. Subjects and Methods Forty-eight patients with obesity [19 males and 29 females, HOMA index ≥ 2.5, age 56.2±6.1 years, body mass index (BMI) 35.9±4.1 kg/m2] were randomly assigned to three 45-day isocaloric VLCKD regimens (≤800 kcal/day) containing whey, plant or animal protein. Anthropometric indexes; blood and urine chemistry, including parameters of kidney, liver, glucose and lipid metabolism; body composition; muscle strength; and taxonomic composition of the gut microbiome were assessed. Adverse events were also recorded. Results Body weight, BMI, blood pressure, waist circumference, HOMA index, insulin, and total and LDL cholesterol decreased in all patients. Patients who consumed whey protein had a more pronounced improvement in muscle strength. The markers of renal function worsened slightly in the animal protein group. A decrease in the relative abundance of Firmicutes and an increase in Bacteroidetes were observed after the consumption of VLCKDs. This pattern was less pronounced in patients consuming animal protein. Conclusions VLCKDs led to significant weight loss and a striking improvement in metabolic parameters over a 45-day period. VLCKDs based on whey or vegetable protein have a safer profile and result in a healthier microbiota composition than those containing animal proteins. VLCKDs incorporating whey protein are more effective in maintaining muscle performance

Predictors of weight loss in patients with obesity treated with a Very Low-Calorie Ketogenic Diet

IntroductionThe Very Low-Calorie Ketogenic Diet (VLCKD) has emerged as a safe and effective intervention for the management of metabolic disease. Studies examining weight loss predictors are scarce and none has investigated such factors upon VLCKD treatment. Among the molecules involved in energy homeostasis and, more specifically, in metabolic changes induced by ketogenic diets, Fibroblast Growth Factor 21 (FGF21) is a hepatokine with physiology that is still unclear.MethodsWe evaluated the impact of a VLCKD on weight loss and metabolic parameters and assessed weight loss predictors, including FGF21. VLCKD is a severely restricted diet (&lt;800 Kcal/die), characterized by a very low carbohydrate intake (&lt;50 g/day), 1.2–1.5 g protein/kg of ideal body weight and 15–30 g of fat/day. We treated 34 patients with obesity with a VLCKD for 45 days. Anthropometric parameters, body composition, and blood and urine chemistry were measured before and after treatment.ResultsWe found a significant improvement in body weight and composition and most metabolic parameters. Circulating FGF21 decreased significantly after the VLCKD [194.0 (137.6–284.6) to 167.8 (90.9–281.5) p &lt; 0.001] and greater weight loss was predicted by lower baseline FGF21 (Beta = −0.410; p = 0.012), male sex (Beta = 0.472; p = 0.011), and central obesity (Beta = 0.481; p = 0.005).DiscussionVLCKD is a safe and effective treatment for obesity and obesity related metabolic derangements. Men with central obesity and lower circulating FGF21 may benefit more than others in terms of weight loss obtained following this diet. Further studies investigating whether this is specific to this diet or to any caloric restriction are warranted