Finding Truth in Cause-Related Advertising: A Lexical Analysis of Brands’ Health, Environment, and Social Justice Communications on Twitter

Consumers increasingly desire to make purchasing decisions based on factors such as health, the environment, and social justice. In response, there has been a commensurate rise in cause-related marketing to appeal to socially-conscious consumers. However, a lack of regulation and standardization makes it difficult for consumers to assess marketing claims; this is further complicated by social media, which firms use to cultivate a personality for their brand through frequent conversational messages. Yet, little empirical research has been done to explore the relationship between cause-related marketing messages on social media and the true cause alignment of brands. In this paper, we explore this by pairing the marketing messages from the Twitter accounts of over 1,000 brands with third-party ratings of each brand with respect to health, the environment, and social justice. Specifically, we perform text regression to predict each brand’s true rating in each dimension based on the lexical content of its tweets, and find significant held-out correlation on each task, suggesting that a brand’s alignment with a social cause can be somewhat reliably signaled through its Twitter communications — though the signal is weak in many cases. To aid in the identification of brands that engage in misleading cause-related communication as well as terms that more likely indicate insincerity, we propose a procedure to rank both brands and terms by their volume of “conflicting” communications (i.e., “greenwashing”). We further explore how cause-related terms are used differently by brands that are strong vs. weak in actual alignment with the cause. The results provide insight into current practices in causerelated marketing in social media, and provide a framework for identifying and monitoring misleading communications. Together, they can be used to promote transparency in causerelated marketing in social media, better enabling brands to communicate authentic valuesbased policy decisions, and consumers to make socially responsible purchase decisions

‘I’m Not a Virus’: Asian Hate in Donald Trump’s Rhetoric

Since the start of Covid-19, anti-Asian sentiment spiked. From March 2020 to June 2021, there were a total of 9,081 self-reported incidents of hate across the United States (Stop AAPI Hate. (2021). As Covid-19 spread into the U.S., President Trump immediately blamed China by referring to the virus as the ‘Chinese Virus’ and used the hashtag #ChineseVirus on Twitter (Weise, E. 2021). Anti-Asian hashtags soared after Donald Trump first tied COVID-19 to China on Twitter. (USA Today. https://www. usatoday.com). Anti-Asian rhetoric expressed on Twitter grew after Trump’s tweet about the ‘Chinese virus,’ and the number of Chinese and other Asian hate crimes grew exponentially. This study explores the rhetorical strategies that Trump utilized to create a sense of fear against the dangerous ‘Other.’ We use a rhetorical thematic analysis to analyze Trump’s tweets that contain language such as ‘Chinese virus’ or ‘Kung Flu.’ Themes such as scapegoating, fear of the other, China bashing, and populist appeals were prevalent. Describing Chinese and other Asian bodies as ‘spreaders’ of diseases, reinforces the Yellow Peril and perpetual foreigner stereotypes. The study shows the importance of presidential rhetoric in influencing public opinion in the context of COVID-19 and Asian hate

Structure of the Entanglement Entropy of (3+1)D Gapped Phases of Matter

We study the entanglement entropy of gapped phases of matter in three spatial dimensions. We focus in particular on size-independent contributions to the entropy across entanglement surfaces of arbitrary topologies. We show that for low energy fixed-point theories, the constant part of the entanglement entropy across any surface can be reduced to a linear combination of the entropies across a sphere and a torus. We first derive our results using strong sub-additivity inequalities along with assumptions about the entanglement entropy of fixed-point models, and identify the topological contribution by considering the renormalization group flow; in this way we give an explicit definition of topological entanglement entropy $S_{\mathrm{topo}}$ in (3+1)D, which sharpens previous results. We illustrate our results using several concrete examples and independent calculations, and show adding "twist" terms to the Lagrangian can change $S_{\mathrm{topo}}$ in (3+1)D. For the generalized Walker-Wang models, we find that the ground state degeneracy on a 3-torus is given by $\exp(-3S_{\mathrm{topo}}[T^2])$ in terms of the topological entanglement entropy across a 2-torus. We conjecture that a similar relationship holds for Abelian theories in $(d+1)$ dimensional spacetime, with the ground state degeneracy on the $d$-torus given by $\exp(-dS_{\mathrm{topo}}[T^{d-1}])$.Comment: 34 pages, 16 figure

Lower-rim ferrocenyl substituted calixarenes: new electrochemical sensors for anions

New ferrocene substituted calix[4 and 5]arenes have been prepared and the crystal structure of a lower-rim substituted bis ferrocene calix[4]arene (7) has been elucidated. The respective ferrocene/ferrocenium redox-couples of compounds 6 (a calix[4]arene tetra ferrocene amide) and 8 (a calix[5]arene pentaferrocene amide) are shown to be significantly cathodically perturbed in the presence of anions by up to 160 mV in the presence of dihydrogen phosphate

Nonlinearity and propagation characteristics of balanced boolean functions

Three of the most important criteria for cryptographically strong Boolean functions are the balancedness, the nonlinearity and the propagation criterion. The main contribution of this paper is to reveal a number of interesting properties of balancedness and nonlinearity, and to study systematic methods for constructing Boolean functions satisfying some or all of the three criteria. We show that concatenating, splitting, modifying and multiplying (in the sense of Kronecker) sequences can yield balanced Boolean functions with a very high nonlinearity. In particular, we show that balanced Boolean functions obtained by modifying and multiplying sequences achieve a nonlinearity higher than that attainable by any previously known construction method. We also present methods for constructing balanced Boolean functions that are highly nonlinear and satisfy the strict avalanche criterion (SAC). Furthermore we present methods for constructing highly nonlinear balanced Boolean functions satisfying the propagation criterion with respect to all but one or three vectors. A technique is developed to transform the vectors where the propagation criterion is not satisfied in such a way that the functions constructed satisfy the propagation criterion of high degree while preserving the balancedness and nonlinearity of the functions. The algebraic degrees of functions constructed are also discussed, together with examples illustrating the various constructions

On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions

We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different kind of approximation guarantee -- weaker than the classical one -- for the difference of submodular (DS) optimization problem, and vice versa. We also illustrate the link between these two problems by analyzing a \textsc{Greedy} algorithm which approximately maximizes objective functions of the form $\Psi(f,g)$, where $f,g$ are two non-negative, monotone, submodular functions and $\Psi$ is a {quasiconvex} 2-variables function, which is non decreasing with respect to the first variable. For the choice $\Psi(f,g)\triangleq f/g$, we recover RS, and for the choice $\Psi(f,g)\triangleq f-g$, we recover DS. To the best of our knowledge, this greedy approach is new for DS optimization. For RS optimization, it reduces to the standard \textsc{GreedRatio} algorithm that has already been analyzed previously. However, our analysis is novel for this case

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.Comment: Published on Journal of the American Chemical Society (2023); 102 pages (18-page manuscript, 84 pages of supporting information

Endurance of SN 2005ip after a decade: X-rays, radio, and H-alpha like SN 1988Z require long-lived pre-supernova mass loss

SN2005ip was a TypeIIn event notable for its sustained strong interaction with circumstellar material (CSM), coronal emission lines, and IR excess, interpreted as shock interaction with the very dense and clumpy wind of an extreme red supergiant. We present a series of late-time spectra of SN2005ip and a first radio detection of this SN, plus late-time X-rays, all of which indicate that its CSM interaction is still strong a decade post-explosion. We also present and discuss new spectra of geriatric SNe with continued CSM interaction: SN1988Z, SN1993J, and SN1998S. From 3-10 yr post-explosion, SN2005ip's H-alpha luminosity and other observed characteristics were nearly identical to those of the radio-luminous SN1988Z, and much more luminous than SNe1993J and 1998S. At 10 yr after explosion, SN2005ip showed a drop in H$\alpha$ luminosity, followed by a quick resurgence over several months. We interpret this variability as ejecta crashing into a dense shell located at around 0.05 pc from the star, which may be the same shell that caused the IR echo at earlier epochs. The extreme H-alpha luminosities in SN2005ip and SN1988Z are still dominated by the forward shock at 10 yr post-explosion, whereas SN1993J and SN1998S are dominated by the reverse shock at a similar age. Continuous strong CSM interaction in SNe~2005ip and 1988Z is indicative of enhanced mass loss for about 1e3 yr before core collapse, longer than Ne, O, or Si burning phases. Instead, the episodic mass loss must extend back through C burning and perhaps even part of He burning.Comment: 14 pages, 8 figs. accepted in MNRA