Social media has become an essential part of the modern lifestyle, with its
usage being highly prevalent. This has resulted in unprecedented amounts of
data generated from users in social media, such as users' attitudes, opinions,
interests, purchases, and activities across various aspects of their lives.
Therefore, in a world of social media, where its power has shifted to users,
actions taken by companies and public figures are subject to constantly being
under scrutiny by influential global audiences. As a result, reputation
management in social media has become essential as companies and public figures
need to maintain their reputation to preserve their reputation capital.
However, domain experts still face the challenge of lacking appropriate
solutions to automate reliable online reputation analysis. To tackle this
challenge, we proposed a novel reputation analysis approach based on the
popular language model BERT (Bidirectional Encoder Representations from
Transformers). The proposed approach was evaluated on the reputational polarity
task using RepLab 2013 dataset. Compared to previous works, we achieved 5.8%
improvement in accuracy, 26.9% improvement in balanced accuracy, and 21.8%
improvement in terms of F-score.

在社交媒体的世界中，维护声誉资本至关重要，但是在自动化可靠的在线声誉分析方面仍面临挑战。因此我们提出了一种基于 BERT 的声誉分析方法，并在 RepLab 2013 数据集上进行了评估。相比之前的工作，我们的准确度提高了 5.8％，平衡准确度提高了 26.9％，F 分数提高了 21.8％。

基于 BERT 的社交媒体声誉分析深度学习方法

A BERT-based Deep Learning Approach for Reputation Analysis in Social Media

Trust and reputation management (TRM) plays an increasingly important role in
large-scale online environments such as multi-agent systems (MAS) and the
Internet of Things (IoT). One main objective of TRM is to achieve accurate
trust assessment of entities such as agents or IoT service providers. However,
this encounters an accuracy-privacy dilemma as we identify in this paper, and
we propose a framework called Context-aware Bernoulli Neural Network based
Reputation Assessment (COBRA) to address this challenge. COBRA encapsulates
agent interactions or transactions, which are prone to privacy leak, in machine
learning models, and aggregates multiple such models using a Bernoulli neural
network to predict a trust score for an agent. COBRA preserves agent privacy
and retains interaction contexts via the machine learning models, and achieves
more accurate trust prediction than a fully-connected neural network
alternative. COBRA is also robust to security attacks by agents who inject fake
machine learning models; notably, it is resistant to the 51-percent attack. The
performance of COBRA is validated by our experiments using a real dataset, and
by our simulations, where we also show that COBRA outperforms other
state-of-the-art TRM systems.

本研究提出了一种名为 COBRA 的框架，用于解决信任和声誉管理中的精度 - 隐私困境，在多代理系统和物联网等大规模在线环境中，通过机器学习模型的嵌入和 Bernoulli 神经网络的聚合，实现代理的隐私保护和更精确的信任评估。

COBRA: 面向上下文的伯努利神经网络用于声誉评估

COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment

Federated learning, as a promising machine learning approach, has emerged to
leverage a distributed personalized dataset from a number of nodes, e.g.,
mobile devices, to improve performance while simultaneously providing privacy
preservation for mobile users. In the federated learning, training data is
widely distributed and maintained on the mobile devices as workers. A central
aggregator updates a global model by collecting local updates from mobile
devices using their local training data to train the global model in each
iteration. However, unreliable data may be uploaded by the mobile devices
(i.e., workers), leading to frauds in tasks of federated learning. The workers
may perform unreliable updates intentionally, e.g., the data poisoning attack,
or unintentionally, e.g., low-quality data caused by energy constraints or
high-speed mobility. Therefore, finding out trusted and reliable workers in
federated learning tasks becomes critical. In this article, the concept of
reputation is introduced as a metric. Based on this metric, a reliable worker
selection scheme is proposed for federated learning tasks. Consortium
blockchain is leveraged as a decentralized approach for achieving efficient
reputation management of the workers without repudiation and tampering. By
numerical analysis, the proposed approach is demonstrated to improve the
reliability of federated learning tasks in mobile networks.

本文介绍了一种基于区块链的可信边缘学习方法，通过引入信誉度指标并利用联盟区块链实现可信度管理，提高了移动网络下的联邦学习任务的可靠性。