4,620 research outputs found
Finding Truth in Cause-Related Advertising: A Lexical Analysis of Brandsâ 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 brandâs 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 brandâs alignment with a social cause can be somewhat reliably signaled through its Twitter communications â 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 âconflictingâ communications (i.e., âgreenwashingâ). 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
âIâm Not a Virusâ: Asian Hate in Donald Trumpâs 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 âChinese Virusâ 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 Trumpâs tweet about the âChinese virus,â 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 âOther.â We use a rhetorical thematic analysis to analyze Trumpâs tweets that contain language such as âChinese virusâ or âKung Flu.â Themes such as scapegoating, fear of the other, China bashing, and populist appeals were prevalent. Describing Chinese and other Asian bodies as âspreadersâ 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 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 in (3+1)D. For the generalized
Walker-Wang models, we find that the ground state degeneracy on a 3-torus is
given by in terms of the topological
entanglement entropy across a 2-torus. We conjecture that a similar
relationship holds for Abelian theories in dimensional spacetime, with
the ground state degeneracy on the -torus given by
.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 , where are two non-negative, monotone,
submodular functions and is a {quasiconvex} 2-variables function, which
is non decreasing with respect to the first variable. For the choice
, we recover RS, and for the choice
, 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
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