推特上的变革性用户可信度评估:基于 RNN 的启发式方法

Vinita Nair, Jyoti Pareek
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引用次数: 0

摘要

研究目的构建一个全面的加权多维模型来评估 Twitter 用户影响力得分的影响,同时考虑基于用户资料、其推文和社交互动的可信度,旨在增强用户辨别假新闻或错误信息的能力。方法:可信度评估是基于文本分析、用户账户属性和用户社交参与度制定的。为了评估可信度,我们使用 Tweepy API 收集了 100 个用户在六个月内发布的约 100,000 条推文。收集到的推文涉及政治、娱乐、商业、科学、体育和热门话题等不同领域。我们选择利用自行设计的深度主动学习模型对未标记的数据进行分类和标记,而不是对收集到的推文进行耗时的人工标注。研究结果递归神经网络、随机森林、奈夫贝叶斯、决策树和支持向量机的影响得分评估准确率分别为 89.03%、79.10%、81.59%、73.06% 和 79.45%。在对结果进行回顾和分析后,RNN 超越了所有其他模型,达到了 89.03% 的超高准确率。新颖性:它采用加权多维框架,通过考虑用户和推文在 Twitter 环境中的可信度,系统地评估了影响力得分。加权特征有助于捕捉不同要素的相对重要性,从而实现更精细、更能感知上下文的决策过程。早期的研究主要集中在单条推文的可信度上,与之相比,我们的研究工作将重点转移到了更广阔的视角,涵盖了用户的可信度、他们的推文以及他们的整体社会影响力。通过纳入用户影响力得分,该框架不仅增强了用户辨别虚假新闻或错误信息的能力,还提高了他们衡量信息可靠性的能力,为新闻可信度分析提供了一种细致入微的方法。关键词主动学习 可信度评分 用户影响力 Twitter 机器学习 循环神经网络
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Transformative User Credibility Assessment on Twitter: A RNN based Heuristic Approach
Objectives: To construct a comprehensive weighted multi-dimensional model to assess the impact of influence score of Twitter users, considering the credibility based on user profile, their tweets and social interactions aiming to empower users in distinguishing fake news or misinformation. Methods: The credibility evaluation is formulated based on text analysis, user account attributes, and user social engagement. We've gathered around 100,000 tweets from 100 users using Tweepy API over a six-month duration for the purpose of evaluating credibility. The collected tweets spanned diverse professions namely politics, entertainment, business, science, sports, and trending topics. We chose to utilize a self-devised deep active learning model to classify and label the unlabelled data instead of engaging in time-consuming human annotation for the tweets we gathered. Findings: The obtained accuracy for influence score evaluation for Recurrent Neural Network, Random Forest, Naïve Bayes, Decision Tree, and Support Vector Machine are 89.03%, 79.10%, 81.59%, 73.06% and 79.45% respectively. Upon reviewing and analysing the outcomes, RNN surpassed all other models achieving an exceptional accuracy of 89.03%. Novelty: Employing a weighted multi-dimensional framework, it systematically evaluates the influence score by considering the credibility of both users and tweets within the context of Twitter. Weighted features are instrumental in capturing the relative importance of different elements, leading to a more refined and context-aware decision-making process. In contrast to earlier research, which predominantly centred on the credibility of individual tweets, our research work shifts the focus to a broader perspective, encompassing the credibility of users, their tweets and their overall social influence. By incorporating user influence score, the framework not only empower users in discerning fake news or mis-information but also elevates their ability to gauge the reliability of information, offering a nuanced approach to news credibility analysis. Keywords: Active Learning, Credibility Score, User Influence, Twitter, Machine Learning, Recurrent Neural Network
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