Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Abstract Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread

Synthesis and Biological Activity Evaluations of Novel Heterobimetallic Platinum(II)–Gold(I) Complexes as Bio-imaging Agents.

Introduction: Platinum-based drugs have become a mainstay of cancer therapy, approximately half of all patients undergoing chemotherapeutic treatment receive a platinum drug. Despite the pervasiveness of platinum drugs in cancer treatment regimens, a number of attendant disadvantages such as resistance to some cancer types and side effects exist. Gold complexes are also emerging as a new class of metal complexes with outstanding cytotoxic properties and are presently being evaluated as potential antitumor agents. Methods and Results: Here, some novel heterobimetallic platinum(II)–gold(I) complexes were synthesized and their cytotoxic activities against different human cancer cell lines such as A549 (human lung cancer), SKOV3 (human ovarian cancer) and MDA-MB-231 (human breast cancer) were evaluated. Electrophoresis mobility shift assay and molecular modeling investigations have been performed to determine the specific binding mode or the binding orientation of these compounds to DNA. Molecular docking studies of them on DNA were performed by means of AutoDock 4.2. Fluorescence emission properties of them were assessed using fluorescent microscopy imaging. In comparison to cis-platin, these compounds displayed significantly higher in vitro cytotoxicity on the studied cell lines. They enter SKOV3 cells rapidly, retaining their phosphorescence and localise simultaneously in cytoplasm, especially in perinuclear regions. So they are suitable candidates for time resolved emission imaging microscopy (TREM). Electrophoresis mobility shift assay showed a little shift and little interaction with plasmid DNA, though this shift is not as much as cis-platin. They may exert their cytotoxic effect through a different mechanism. Conclusions: According to the results, careful drug design would result in producing potential antitumor agents with high efficacy. These Pt(II)-Au(I) complexes can be used in biological labelling and cellular imaging studies, due to desirable absorption and emission of them in solution under ambient conditions. Hence, they had a potential value for drug development as anticancer agents

An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web

Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate from 2013. We formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative network. The problem of reconstructing the underlying structure is posed as a latent model estimation problem. We automatically extract and aggregate the actants and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct a network, which constitutes the underlying narrative framework. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual one, which may develop more slowly as revelations come to light.Comment: conspiracy theory, narrative structur

Towards the Application of Uncertainty Analysis in Architectural Design Decision-Making

To this day, proper handling of uncertainties -including unknown variables in primary stages of a design, an actual climate data, occupants` behavior, and degradation of material properties over the time- remains as a primary challenge in an architectural design decision-making process. For many years, conventional methods based on the architects' intuition have been used as a standard approach dealing with uncertainties and estimating the resulting errors. However, with buildings reaching great complexity in both their design and material selections, conventional approaches come short to account for ever-existing but unpredictable uncertainties and prove incapable of meeting the growing demand for precise and reliable predictions. This study aims to develop a probability-based framework and associated prototypes to employ uncertainty analysis and sensitivity analysis in architectural design decision-making. The current research explores an advanced physical model for thermal energy exchange characteristics of a hypothetical building and uses it as a test case to demonstrate the proposed probability-based analysis framework. The proposed framework provides a means to employ uncertainty and sensitivity analysis to improve reliability and effectiveness in a buildings design decision-making process

Identification of medicinal plants effective in infectious diseases in Urmia, northwest of Iran

Objective: To identify the medicinal plants effective in infectious diseases. Methods: Initially, we obtained a list of herbalists and traditional healers from Food and Drug Deputy. Direct observations and interviews as well as collection of herbarium specimens of indigenous medicinal plants effective in infectious diseases of urinary tract, reproductive, digestive, respiratory and skin systems were performed. This study was conducted through questionnaires and interviews; the questionnaires were distributed among traditional healers and simultaneous interviews were also run. The plants were herbariumized, herbarium specimens were authenticated, and their species were determined by using reliable flora and other sources. Finally, the data were input into Excel 2010 and analyses were performed. Results: Out of the studied plants, 35 native medicinal plants belonging to 17 families were effective in the treatment of various diseases and infections. In this study, the Lamiaceae family had the highest frequency of plants for the treatment of infections. Traditional healers of Urmia in 24% of cases used the leaves of medicinal herb to treat patients. In 68% of cases, they prescribed medicinal herbs in the boiled forms. Most medicinal herbs showed therapeutic effect on the digestive system. Conclusions: Traditional medicinal sources, valuable knowledge of traditional healers in Urmia, the scientific investigation of the effects of the herbs offered in this study and their effects in traditional medicine may provide a good source for new drugs in modern medicine

An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com

Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of > 80% and an average edge detection rate of >89\%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others

Cyclometalated platinum(II) complexes of 2,2'-bipyridine N-oxide containing 1,1'-bis(diphenylphosphino)ferrocene ligand: Structural, computational and electrochemical studies

The preparation and characterization of new heteronuclear-platinum(II) complexes containing 1,1'-bis(diphenylphosphino)ferrocene (dppf) ligand are described. The reaction of the known starting complex [PtMe(κ2N,C-bipyO-H)(SMe2)], A, in which bipyO-H is a cyclometalated “rollover” 2,2'-bipyridine N-oxide, with the dppf ligand in a 2 : 1 ratio or an equimolar ratio led to the formation of corresponding binuclear complex [Pt2Me2(κ2N,C-bipyO-H)2(µ-dppf)], 1, or mononuclear complex [PtMe(κ1C-bipyO-H)(dppf)], 2, respectively. According to the reaction conditions, the dppf ligand in 1 and 2 behaves as either a bridging or chelateing ligand. All complexes were characterized by NMR spectroscopy. The solid-state structure of 2 was determined by single-crystal X-ray diffraction method and it was shown that the chelateing dppf ligand in this complex was arranged “synclinal-staggered” conformation. Also, the occurrence of intermolecular C–HCp…ObipyO-H interactions in the solid-state gave rise to an extended 1-D network. The electronic absorption spectra and the electrochemical behavior of these complexes are discussed. Density functional theory (DFT) was used for geometry optimization of the singlet states in solution and for electronic structure calculations. The analysis of the molecular orbital (MO) compositions in terms of occupied and unoccupied fragment orbitals in 2 was performed