Fake News Detection on Twitter Using Propagation Structures

The growth of social media has revolutionized the way people access information. Although platforms like Facebook and Twitter allow for a quicker, wider and less restricted access to information, they also consist of a breeding ground for the dissemination of fake news. Most of the existing literature on fake news detection on social media proposes user-based or content-based approaches. However, recent research revealed that real and fake news also propagate significantly differently on Twitter. Nonetheless, only a few articles so far have explored the use of propagation features in their detection. Additionally, most of them have based their analysis on a narrow tweet retrieval methodology that only considers tweets to be propagating a news piece if they explicitly contain an URL link to an online news article. By basing our analysis on a broader tweet retrieval methodology that also allows tweets without an URL link to be considered as propagating a news piece, we contribute to fill this research gap and further confirm the potential of using propagation features to detect fake news on Twitter. We firstly show that real news are significantly bigger in size, are spread by users with more followers and less followings, and are actively spread on Twitter for a longer period of time than fake news. Secondly, we achieve an 87% accuracy using a Random Forest Classifier solely trained on propagation features. Lastly, we design a Geometric Deep Learning approach to the problem by building a graph neural network that directly learns on the propagation graphs and achieve an accuracy of 73.3%

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

The influence of traits associated with autism spectrum disorder (ASD) on the detection of fake news.

It has been suggested that neuro-diverse individuals may be particularly good at detecting online deception (Pick 2019). A small-scale exploratory study was conducted to investigate whether individuals with traits associated with Autism Spectrum Disorder (ASD) were more or less accurate in spotting different types of fake news. A non-clinical sample of university students completed an online identification task, where both fake and real articles items were manipulated in terms of their emotive content. When individuals with low and high scores on the Autism-Spectrum Quotient (Baron-Cohen et al. 2001) were compared, there were no significant main effects on detection accuracy. However, there were two significant interactions, indicating an interesting relationship between message emotiveness, ASD and fake news detection. The results contribute to an understanding of how psychological differences, in particular ASD, may affect online judgements and will contribute to a developing body of work relating positive skills of neuro-diverse individuals to the cybersecurity industry

Assessing the impact of a health intervention via user-generated Internet content

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign

Non-Small Cell Lung Carcinoma Cell Motility, Rac Activation and Metastatic Dissemination Are Mediated by Protein Kinase C Epsilon

Background: Protein kinase C (PKC) e, a key signaling transducer implicated in mitogenesis, survival, and cancer progression, is overexpressed in human primary non-small cell lung cancer (NSCLC). The role of PKCe in lung cancer metastasis has not yet been established. Principal Findings: Here we show that RNAi-mediated knockdown of PKCe in H358, H1299, H322, and A549 NSCLC impairs activation of the small GTPase Rac1 in response to phorbol 12-myristate 13-acetate (PMA), serum, or epidermal growth factor (EGF). PKCe depletion markedly impaired the ability of NSCLC cells to form membrane ruffles and migrate. Similar results were observed by pharmacological inhibition of PKCe with eV1-2, a specific PKCe inhibitor. PKCe was also required for invasiveness of NSCLC cells and modulated the secretion of extracellular matrix proteases and protease inhibitors. Finally, we found that PKCe-depleted NSCLC cells fail to disseminate to lungs in a mouse model of metastasis. Conclusions: Our results implicate PKCe as a key mediator of Rac signaling and motility of lung cancer cells, highlighting its potential as a therapeutic target

Social features of online networks: the strength of intermediary ties in online social media

An increasing fraction of today social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite of this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties), events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).Comment: 14 pages, 18 figure

The interpretations and uses of fitness landscapes in the social sciences

__Abstract__ This working paper precedes our full article entitled “The evolution of Wright’s (1932) adaptive field to contemporary interpretations and uses of fitness landscapes in the social sciences” as published in the journal Biology & Philosophy (http://link.springer.com/article/10.1007/s10539-014-9450-2). The working paper features an extended literature overview of the ways in which fitness landscapes have been interpreted and used in the social sciences, for which there was not enough space in the full article. The article features an in-depth philosophical discussion about the added value of the various ways in which fitness landscapes are used in the social sciences. This discussion is absent in the current working paper. Th

Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The Claude Bernard Lecture 2009

Insulin resistance is a hallmark of type 2 diabetes mellitus and is associated with a metabolic and cardiovascular cluster of disorders (dyslipidaemia, hypertension, obesity [especially visceral], glucose intolerance, endothelial dysfunction), each of which is an independent risk factor for cardiovascular disease (CVD). Multiple prospective studies have documented an association between insulin resistance and accelerated CVD in patients with type 2 diabetes, as well as in non-diabetic individuals. The molecular causes of insulin resistance, i.e. impaired insulin signalling through the phosphoinositol-3 kinase pathway with intact signalling through the mitogen-activated protein kinase pathway, are responsible for the impairment in insulin-stimulated glucose metabolism and contribute to the accelerated rate of CVD in type 2 diabetes patients. The current epidemic of diabetes is being driven by the obesity epidemic, which represents a state of tissue fat overload. Accumulation of toxic lipid metabolites (fatty acyl CoA, diacylglycerol, ceramide) in muscle, liver, adipocytes, beta cells and arterial tissues contributes to insulin resistance, beta cell dysfunction and accelerated atherosclerosis, respectively, in type 2 diabetes. Treatment with thiazolidinediones mobilises fat out of tissues, leading to enhanced insulin sensitivity, improved beta cell function and decreased atherogenesis. Insulin resistance and lipotoxicity represent the missing links (beyond the classical cardiovascular risk factors) that help explain the accelerated rate of CVD in type 2 diabetic patients