Helpfulness

Abstract: The mass media are ranked with respect to their perceived helpfulness in satisfying clusters of needs arising from social roles and individual dispositions. For example, integration into the sociopolitical order is best served by newspaper; while "knowing oneself " is best served by books. Cinema and books are more helpful as means of "escape" than is television. Primary relations, holidays and other cultural activities are often more important than the mass media in satisfying needs. Television is the least specialized medium, serving many different personal and political needs. The "interchangeability" of the media over a variety of functions orders televisions, radio, newspapers, books, and cinema in a circumplex. We speculate about which attributes of the media explain the social and psychological needs they serve best. The data, drawn from an Israeli survey, are presented as a basis for cross-cultural comparison. Disciplines Communication | Social and Behavioral Sciences This journal article is available at ScholarlyCommons: http://repository.upenn.edu/asc_papers/267 ON THE USE OF THE MASS MEDIA FOR IMPORTANT THINGS * ELIHU KATZ MICHAEL GUREVITCH

Abstract: Money has been said to change people's motivation (mainly for the better) and their behavior toward others (mainly for the worse) The results of nine experiments suggest that money brings about a self-sufficient orientation in which people prefer to be free of dependency and dependents Reminders of money, relative to nonmoney reminders, led to reduced requests for help and reduced helpfulness toward others Relative to participants primed with neutral concepts, participants primed with money preferred to play alone, work alone, and put more physical distance between themselves and a new acquaintance

Abstract: In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Abstract: With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.