Target-dependent sentiment classification (TSC) enables a fine-grained automatic analysis of sentiments expressed in texts. Sentiment expression varies depending on the domain, and it is necessary to create domain-specific datasets.While socially important, TSC in the news domain remains relatively understudied.We introduce MAD-TSC, the first multilingual aligned dataset designed for TSC in news.MAD-TSC differs substantially from existing resources.First, it includes aligned examples in eight languages to facilitate a comparison of performance for individual languages, and a direct comparison of human and machine translation.Second, the dataset is sampled from a diversified parallel news corpus, and is diversified in terms of news sources and geographic spread of entities.Finally, MAD-TSC is more challenging than existing datasets because its samples are more complex.We exemplify the use of MAD-TSC with comprehensive monolingual and multilingual experiments.The latter shows that machine translations can successfully replace manual ones, and that performance for all included languages can match that of English by automatically translating test examples.

Online social networks use AI techniques to automatically infer profiles from users’ shared data. However, these inferences and their effects remain, to a large extent, opaque to the users themselves. We propose a method which raises user awareness about the potential use of their profiles in impactful situations, such as searching for a job or an accommodation. These situations illustrate usage contexts that users might not have anticipated when deciding to share their data. User photographic profiles are described by automatic object detections in profile photos, and associated object ratings in situations. Human ratings of the profiles per situation are also available for training. These data are represented as graph structures which are fed into graph neural networks in order to learn how to automatically rate them. An adaptation of the learning procedure per situation is proposed since the same profile is likely to be interpreted differently, depending on the context. Automatic profile ratings are compared to one another in order to inform individual users of their standing with respect to others. Our method is evaluated on a public dataset, and consistently outperforms competitive baselines. An ablation study gives insights about the role of its main components.

Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent years gives the impression that the task is practically solved. Here, we revisit the problem and argue that existing evaluation datasets were built using two oversimplifying design choices. First, the usual identity selection to form imposter pairs is not challenging enough because, in practice, verification is needed to detect challenging imposters. Second, the underlying demographics of existing datasets are often insufficient to account for the wide diversity of facial characteristics of people from across the world. To mitigate these limitations, we introduce the F aV CI2D dataset. Imposter pairs are challenging because they include visually similar faces selected from a large pool of demographically diversified identities. The dataset also includes metadata related to gender, country and age to facilitate fine-grained analysis of results. F aV CI2D is generated from freely distributable resources. Experiments with state-of-the-art deep models that provide nearly 100% performance on existing datasets show a significant performance drop for F aV CI2D, confirming our starting hypothesis. Equally important, we analyze legal and ethical challenges which appeared in recent years and hindered the development of face analysis research. We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer. F aV CI2D is available at https://github.com/AIMultimediaLab/FaVCI2D-Face-Verification-with-Challenging-Imposters-and-Diversified-Demographics.

Social networks give free access to their services in exchange for the right to exploit their users’ data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users’ photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation. These components are combined in LERV UP, a method which learns to rate visual user profiles in each situation. LERV UP exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERV UP is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed.