Person- versus content-oriented approaches in English and German email responses to customer complaints: a cross-cultural analysis of moves and first-person pronouns

This cross-cultural study examines the differences in communicative styles between English and German email responses to customer complaints by analysing their discourse structure (through a rhetorical move analysis) and the frequency of first-person references (I and we and their different forms). The framework is given by House (House, Juliane. 2006. Communicative styles in English and German. European Journal of English Studies 10(3). 249–267.), who suggests that English speakers tend to use a more interpersonal (i.e., people-oriented) communicative style, while German speakers show a preference for a transactional (i.e., content-oriented) style. In addition, first-person references within the genre of email responses to complaints are associated with either the customer service agent’s personal or corporate identity. The data consist of 150 English and 84 German authentic emails. The results of the move analysis reveal that the discourse structure of both data sets is mainly similar, but the few differences point into the direction of support for House’s framework, in particular the dimension on addressee- or content-orientation. Although agents generally use more we than I-references in both data sets, thus exhibiting mainly a corporate identity, they tend to use the opposite in some moves (e.g., Apology), which points to pronominal shifting across move level, as suggested in previous research (Zhang, Yi & Camilla Vásquez. 2014. Hotels’ responses to online reviews: Managing consumer dissatisfaction. Discourse, Context and Media 6. 54–64.). Overall, the German agents use more we-references compared to their British colleagues. Finally, agents use pronominal shifting within move level to distance themselves from the company.

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

PurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.Design/methodology/approachBased on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.FindingsOur findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.Research limitationsThe analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com.Practical implicationsThe results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.Originality/valueTo the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.