Zoekresultaten

The Role of Fake Review Detection in Managing Online Corporate Reputation

In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google, 2019). Also, governmental authorities started acting against organisations that show to have a high number of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals, it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the reputation of an organisation and lead to a disvalued source to determine the public opinion about brands. Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied for a while now, there are only a limited number of spam detection models available for companies to protect their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on online presence and limit the amount of negative input that affects their competitive position which can even lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam detector based on supervised machine learning is derived in an experiment that performs quite well on the well-known Amazon Mechanical Turk dataset.

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.

A stage to engage: social media use and corporate reputation

Corporate reputation is a valuable intangible asset for companies, yet is increasingly difficult to manage in an era with hard-to-control online conversations. In this paper, we investigate whether and when a company's online activities to acquire engaged consumers are beneficial for corporate reputation. In a survey among 3531 customers and non-customers of an international airline, we measured consumers' engagement in the airline's social media activities and perception of corporate reputation. Results show that consumers' intensity of social media use is positively related to their engagement in the airline's social media activities, especially among customers. Engagement in the social media activities in turn is positively related to corporate reputation, especially among non-customers. We discuss the implications of the results for social media policies in the travel and tourism industry.

Exploring Corporate Reputation based on Sentiment Polarities That Are Related to Opinions in Dutch Online Reviews

This research demonstrates the power and robustness of the vocabulary method by Hernández-Rubio et al. (2019) for aspect extraction from online review data. We showcase that this algorithm not only works on the English language based on the CoreNLP toolkit, but also extend it on the Dutch language, specifically with aid of the Frog toolkit. Results on sampled datasets for three different retailers show that it can be used to extract fine-grained aspects that are relevant to acquire corporate reputation insights.

Online conversation and corporate reputation: a two-wave longitudinal study on the effects of exposure to the social media activities of a highly interactive company

In this paper, we investigate whether and to what extent exposure to a company's social media activities over time is beneficial for corporate reputation, and whether conversational human voice mediates this relation. In a two-wave longitudinal survey among 1969 respondents, we assessed consumers' exposure to an international airline's social media activities, perceived level of conversational human voice and perception of corporate reputation. The results show that consumers' level of exposure to company social media activities precedes perceptions of corporate reputation. Also, conversational human voice mediates the relation between consumers' level of exposure to company social media activities and perceptions of corporate reputation. We discuss the implications of the results for the presence of organizations in social media.

A company’s corporate reputation through the eyes of employees measured with sentiment analysis of online reviews