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Register a new userWhats in a Name? Understanding Profile Name Reuse on Twitter
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Added by
Gianluca Stringhini
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Users on Twitter are commonly identified by their profile names. These names are used when directly addressing users on Twitter, are part of their profile page URLs, and can become a trademark for popular accounts, with people referring to celebrities by their real name and their profile name, interchangeably. Twitter, however, has chosen to not permanently link profile names to their corresponding user accounts. In fact, Twitter allows users to change their profile name, and afterwards makes the old profile names available for other users to take. In this paper, we provide a large-scale study of the phenomenon of profile name reuse on Twitter. We show that this phenomenon is not uncommon, investigate the dynamics of profile name reuse, and characterize the accounts that are involved in it. We find that many of these accounts adopt abandoned profile names for questionable purposes, such as spreading malicious content, and using the profile names popularity for search engine optimization. Finally, we show that this problem is not unique to Twitter (as other popular online social networks also release profile names) and argue that the risks involved with profile-name reuse outnumber the advantages provided by this feature.
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Active Galactic Nuclei (AGN) are energetic astrophysical sources powered by accretion onto supermassive black holes in galaxies, and present unique observational signatures that cover the full electromagnetic spectrum over more than twenty orders of magnitude in frequency. The rich phenomenology of AGN has resulted in a large number of different flavours in the literature that now comprise a complex and confusing AGN zoo. It is increasingly clear that these classifications are only partially related to intrinsic differences between AGN, and primarily reflect variations in a relatively small number of astrophysical parameters as well the method by which each class of AGN is selected. Taken together, observations in different electromagnetic bands as well as variations over time provide complementary windows on the physics of different sub-structures in the AGN. In this review, we present an overview of AGN multi-wavelength properties with the aim of painting their big picture through observations in each electromagnetic band from radio to gamma-rays as well as AGN variability. We address what we can learn from each observational method, the impact of selection effects, the physics behind the emission at each wavelength, and the potential for future studies. To conclude we use these observations to piece together the basic architecture of AGN, discuss our current understanding of unification models, and highlight some open questions that present opportunities for future observational and theoretical progress.
We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on several tasks while such perturbations are natural, state of the art trained models are surprisingly brittle. The brittleness continues even with the recent entity-aware BERT models. We also try to discern the cause of this non-robustness, considering factors such as tokenization and frequency of occurrence. Then we provide a simple method that ensembles predictions from multiple replacements while jointly modeling the uncertainty of type annotations and label predictions. Experiments on three NLP tasks show that our method enhances robustness and increases accuracy on both natural and adversarial datasets.
Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly -- to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework -- CONNA -- to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21%-19.84% improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner -- a large online academic search system.
A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation.
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individuals true occupation and a word embedding of their name. This method leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes. Crucially, it only requires access to individuals names at training time and not at deployment time. We evaluate two variations of our proposed method using a large-scale dataset of online biographies. We find that both variations simultaneously reduce race and gender biases, with almost no reduction in the classifiers overall true positive rate.
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