Attracting applicants through the organization’s social media page : signaling employer brand personality

The purpose of this study is to examine how potential applicants’ exposure to an organization’s social media page relates to their subsequent organizational attractiveness perceptions and word-of-mouth intentions. Based on signaling theory and the theory of symbolic attraction, we propose that potential applicants rely on perceived communication characteristics of the social media page (social presence and informativeness) as signals of the organization’s employer brand personality (warmth and competence), which in turn relate to organizational attractiveness and word-of-mouth. Data were gathered in a simulated job search process in which final-year students looked for an actual job posting and later visited an actual organization’s social media page. In line with our hypotheses, results show that the perceived social presence of a social media page was indirectly positively related to attractiveness and word-of-mouth through its positive association with perceived organizational warmth. Perceived informativeness was indirectly positively related to these outcomes through its positive association with perceived organizational competence. In addition, we found that social presence was also directly positively related to organizational attractiveness. These findings suggest that organizations can use social media pages to manage key recruitment outcomes by signaling their employer brand personality

Social media recruitment : communication characteristics and sought gratifications

This study examines how social media pages can be used to influence potential applicants' attraction. Based on the uses and gratifications theory, this study examines whether organizations can manipulate the communication characteristics informativeness and social presence on their social media page to positively affect organizational attractiveness. Moreover, we examine whether job applicants' sought gratifications on social media influence these effects. A 2 x 2 between-subjects experimental design is used. The findings show that organizations can manipulate informativeness and social presence on their social media. The effect of manipulated informativeness on organizational attractiveness depends on the level of manipulated social presence. When social presence was high, informativeness positively affected organizational attractiveness. This positive effect was found regardless of participants' sought utilitarian gratification. Social presence had no significant main effect on organizational attractiveness. There was some evidence that the effect of social presence differed for different levels of social gratification

Why We Need New Evaluation Metrics for NLG

The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is data- and system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.Comment: accepted to EMNLP 201

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods