Love and Power: Grau and Pury (2014) as a Case Study in the Challenges of X-Phi Replication

Grau and Pury (Review of Philosophy and Psychology, 5, 155–168, 2014) reported that people’s views about love are related to their views about reference. This surprising effect was however not replicated in Cova et al.’s (in press) replication study. In this article, we show that the replication failure is probably due to the replication’s low power and that a metaanalytic reanalysis of the result in Cova et al. suggests that the effect reported in Grau and Pury is real. We then report a large, highly powered replication that successfully replicates Grau and Pury 2014. This successful replication is a case study in the challenges involved in replicating scientific work, and our article contributes to the discussion of these challenges

General Sentiment Decomposition: opinion mining based on raw Natural Language text

The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new customer. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (as well as citizens' trust is for a politician). Textual data have then proved extremely useful, but they are complex, as the language is. For that, many approaches focus more on producing well-performing classifiers and ignore the highly complex interpretability of their models. Instead, we propose a framework able to produce a good sentiment classifier with a particular focus on the model interpretability. After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a Natural Language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of complex interpretation (usually considered as black-boxes), with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. In fact, the General Sentiment Decomposition (GSD) framework that we propose is based on a combination of Threshold-based Naive Bayes (an improved version of the original algorithm), SentiWordNet (an enriched Lexical Database for Sentiment Analysis tasks), and the Words Embeddings features (a high dimensional representation of words) that directly comes from the usage of Neural Networks. Moreover, using the GSD framework, we assess an objective sentiment scoring that improves the results' interpretation in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, find the company's strengths (useful for advertising campaigns), and, if time information is present, analyze trends on macro/micro topics. Besides, we have to consider that NL text data can be associated (or not) with a sentiment label, for example: 'positive' or 'negative'. To support further decision-making, we apply the proposed method to labeled (Booking.com, TripAdvisor.com) and unlabelled (Twitter.com) data, analyzing the sentiment of people who discuss a particular issue. In this way, we identify the aspects perceived as critical by the people concerning the "feedback" they publish on the web and quantify how happy (or not) they are about a specific problem. In particular, for Booking.com and TripAdvisor.com, we focus on customer satisfaction, whilst for Twitter.com, the main topic is climate change

Latina/o Conversion and Miracle-Seeking at a Buddhist Temple

The growing diversification of the US Latino religious’ experiences calls for scholarly attention beyond Protestant or Catholic categories. This study begins to answer this call. Using interview data with 26 Latinos collected over 2 years of observation at the True Lama Meditation Center (TLMC) in Houston, Texas, we describe how Latinos who convert to Buddhism or actively attend the temple while also continuing to attend Christian services (both Catholic and Protestant) see themselves and understand their religious identities and practices. We then explore the reasons for their conversion or changes in religious identities and practices through various theoretical lens. Although the majority of respondents now claim to be Buddhist, many did not switch religions but augmented or extended their religious identities and practices. Reasons for conversion to Buddhism or concurrent involvement at the temple and Buddhist faith practices include seeking material support and miracles and those seeking spiritual fulfillment they felt they were not getting in Christian faith practices

Will-they-won't-they: A very large dataset for stance detection on twitter

We present a new challenging stance detection dataset, called Will-They-Won’t-They (WT--WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection. Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain.Keynes Fund, Cambridg

Social interactions or business transactions? What customer reviews disclose about Airbnb marketplace

Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests’ reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods

Sentiment Analysis on Product-Service Systems

The main goal of this dissertation is to develop a tool to help each company reduce the amount of failed product-service systems that were avoidable due previous experience. By using tools and ideas already available and build them in a way they can interact with each other, this tool aims to give designers a better as faster way to view data. This was identified as a possible improvement since for the past 20 years the economy evolved into a consumer driven market, this led to the development of an extremely competitive economy. Companies need to strive for innovation and quality of products and services, faster than never. Products and services also need to match the expectations and needs of customers. Analyzing where product and service systems are lacking in terms of customer requirements is crucial. Currently it might take some time for information to travel from customer to producer, since the connection may include stores and local representatives before reaching the products’ and services’ designers. Although this information is readily available in social networks, the issue resides in efficiently merging and showing it in a simple and meaningful way to the designer of new products and systems. By shortening the time spent for information travel between costumer and producer, might lead to better and more innovative products

"Don’t Downvote A\$\$\$\$\$\$s!!": An Exploration of Reddit’s Advice Communities

Advice forums are a crowdsourced way to reinforce cultural norms and moral behavior. Sites like Reddit contain massive amounts of natural language human interaction, with rules and norms unique to each individual subreddit community. To explore this data, we created a dataset with top 1000 posts from each of two such forums, r/AmItheAsshole and r/relationships, and extracted natural language features including sentiment, similarity, word frequency, and demographics using both algorithmic and manual methods. Further, we developed a method to extract demographic information from the subreddits, examined how the post authors’ self-disclosures reflect the unique communities in which their posts are shared, and discussed how the authors’ language use choices might be related to broader social patterns. We observed some differences between the subreddits in terms of word frequency, demographics disclosure, and gendered language. In general, both subreddits had more female posters than male, and posters tended to use more words about their opposite gender than the same. Gender-diverse posters were uncommon. Implications for future research include a more careful, inclusive focus on identity and disclosure and how that interacts with advice-seeking behavior in online communities

CSR, Big Data, and Accounting: Firms' Use of Social Media for CSR-Focused Reporting, Accountability, and Reputation Gain

The rise of Big Data, particularly social media, is engendering considerable disruptions in the ways in which firms and stakeholders communicate about firm-relevant issues. The effect of social media appears to be particularly strong in the domain of corporate social responsibility (CSR). This thesis presents three empirical studies on Fortune 200 firms use of social media to engage in CSR-related activities. All three studies rely on original 2014 data related to the 42 CSR-focused Twitter accounts maintained by the US-based Fortune 200 companies comprising 18,722 firm messages and 163,402 messages sent by members of the public. This thesis first examines the outcomes of firms social media-based CSR engagement, building a theoretical argument about the reputational benefits, or reputational capital, acquired by firms through the messages they send on social media. It then turns to an investigation of the publics discussion of the companies CSR activities; this second study relies on inductive analyses to build insights into the nature of the firm-centered CSR messages sent by members of the public, the nature of firms reactions to these public messages, and the relationship between the two. The third and final study refines and then empirically tests the causal model developed in the second study. Collectively, these three studies shed light on the nature of the micro-reporting and micro-accountability behaviors that appear to characterize firms CSR efforts on social media sites. The thesis concludes with a summary of the implications of these new behaviors for the accounting and CSR literatures