Twitter led a remarkable breakthrough in information sharing on online social media. The eminent technology can propagate a piece of rumor to a large community of people in a short period. The timely detection of rumor tweets in social media curtails panic among the public during critical situations. Traditional machine learning techniques are not capable of categorizing rumor information effectively. To address this problem, the author has proposed a novel neural network approach called veracity detection neural network for identifying the rumor-related Twitter posts’ content in real-time events. This algorithm utilized the convolutional sentence encoder–bi-directional long short-term memory (CSE-BiLSTM) model with pre-trained vectorization methods such as Word2vec, fastText and universal sentence encoder (USE). The hybrid CSE-BiLSTM with USE vectorization technique yields the best results for the performance metrics of accuracy, F1-score, precision and recall. The proposed algorithm achieves 90.56%, 86.18% and 93.89% accuracy values to classify the tweet into rumor or non-rumor for the datasets such as PHEME, newly emerged rumors on Twitter and #gaja, respectively. Finally, a comparative study shows that the proposed neural network model outperformed all other existing rumor text classification systems.

中文翻译：

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社交媒体内容中文本的谣言识别和验证

Twitter 在在线社交媒体上的信息共享方面取得了显着突破。卓越的技术可以在短时间内将一条谣言传播到一大群人。及时发现社交媒体上的谣言推文可以减少公众在危急情况下的恐慌。传统的机器学习技术无法有效地对谣言信息进行分类。为了解决这个问题，作者提出了一种新的神经网络方法，称为真实性检测神经网络，用于在实时事件中识别与谣言相关的 Twitter 帖子的内容。该算法利用卷积句子编码器-双向长短期记忆 (CSE-BiLSTM) 模型和预训练的向量化方法，如 Word2vec、fastText 和通用句子编码器 (USE)。具有 USE 矢量化技术的混合 CSE-BiLSTM 在准确度、F1 分数、精度和召回率的性能指标方面产生了最佳结果。所提出的算法分别达到了 90.56%、86.18% 和 93.89% 的准确度值，以将推文分类为 PHEME、Twitter 上新出现的谣言和#gaja 等数据集的谣言或非谣言。最后，一项比较研究表明，所提出的神经网络模型优于所有其他现有的谣言文本分类系统。