Enhancing User Security on Instagram: A Multifaceted AI System for Filtering Abusive Comments

Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Additional comments on foot reflexology treatment for sensorineural hearing loss in infant

Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity changes before and after treatment,as indicated by functional magnetic resonance imaging.Hence,this commentary discusses the case and our viewpoints regarding foot reflexology for treating SNHL.

Emotional differences based on comments on doctor-patient disputes with varying levels of severity

BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.

LETTERS FROM READERS:Comments More Valuable Than Gold

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Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect Sentiment Quad Prediction(ASQP)is a novel and complete subtask.ASQP aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of opinion.Nevertheless,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in ASQP,it is poor for complex cyber comment processing.Also,most research has focused on processing English text,and ASQP for Chinese text is almost non-existent.Chinese usage is more casual than English,and individual characters contain more information.We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies.In this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure.Besides,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1.Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

A Study on the Acceptance of the English Translation of Romance of the Three Kingdoms by Overseas Readers Based on Python Data Analysis Technology

Paperless reading has become a prevalent trend among global readers,leading to the accumulation of vast amounts of reading data on numerous book websites.This offers new perspectives for studying translated works.This paper utilizes Python-based data processing technology to collect and analyze reader reviews of Romance of the Three Kingdoms on Amazon and Goodreads,presenting trends in review volume,word cloud maps,and readers’emotional attitudes in a quantitative manner.The findings indicate that overseas readers generally exhibit a positive emotional tendency towards Romance of the Three Kingdoms and recognize its cultural value.However,negative opinions do exist,focusing on aspects of the book’s quality,such as printing quality and proofreading.These results provide valuable insights for the foreign translation of canonical texts.

基于LDA主题模型的中国图书出口效果研究——以亚马逊海外读者评论为例

中华文化通过图书走向世界舞台,是提高国家文化软实力、讲好中国故事、塑造良好国家形象的有效途径。通过Python语言工具对美国亚马逊网站上的中国图书评论数据进行分析,构建LDA主题模型,探讨中国图书出口效果,深入挖掘海外读者对中国出口图书的认知评价。研究发现,海外读者对中国图书的关注点集中于内容评价、翻译理解、服务体验、中国元素、题材情节5个方面。针对以上关注点,从图书内容、出版传播、读者反馈等角度提出了具有实践价值的建议,为推动中国图书高质量出口提供了理论参考和解决思路。

媒介文化的折射:在线读书会读者的评论特征研究

数字化阅读和社交化分享催生出网络读书会的新景观。弹幕评论形式的出现,使在线读书会生成了基于播放时间和读者评论时间两种不同时间线的读者评论文本。文章对比分析传统评论和弹幕评论在行为和内容两方面的不同特征,认为不同的评论形式会显著影响读者的评论内容。具体而言,弹幕更倾向于社交娱乐的浅层交流,从时间维度延长读书会的讨论热度;而传统评论则更聚焦书籍内容的讨论,在空间维度呈现深度讨论特征。通过回溯阅读交流的历史场景,发现评论媒介偏向的背后折射出以原生口语文化、书面文化和次生口语文化为表征的媒介文化已在环境和形式两个方面,以其内化的主导性传播模式塑造了在线读书会读者的思维方式和交流模式,从而为数字时代的读者评论行为特征提供了理论注解。

《文心雕龙》英译中的读者意识——以宇文所安英译本为例

读者不是译文的消极消费者,而是译文意义的能动建构者。读者意识是译者以读者视角观照翻译、使翻译行为围绕读者中心逐步向前推进的心理机制。宇文所安的读者意识体现在内容导向、沟通途径、主流诗学和读者接受四个方面,表现为材料选择的贴近意识、思想交流的贴心意识、表达习惯的贴合意识、服务读者的体贴意识。得益于读者意识,宇文所安充分履行了译者的职责,妥善处理了译者、作者和读者的三方关系,促进了《文心雕龙》在英语世界的接受与传播。中国文论要走向世界,译者的翻译能力是前提与基础,而读者意识是确保译者翻译能力得到充分展示的思想保证。读者意识有助于译文走进读者的心灵世界,有利于译文在译入语文化语境的进一步传播。

生成式人工智能支持的教师评语研究:基于初中数学课堂的实践探索

教师评语是形成性评价分析的重要数据来源,但现有教师评语生成的质量在社会性、客观性和个性化方面存在一定的不足,生成式人工智能支持的教师评语为解决该问题提供了可能。生成式教师评语是指教师通过认知诊断技术得到的智能诊断数据与生成式人工智能平台进行交互,从而让生成式人工智能平台模拟教师社会性语言,生成数据驱动的个性化教师评语。在初中数学课堂中的实证研究发现:生成式教师评语有效地提高了学生的自我调节学习能力(Cohen's d=1.08,p=0.000)和学习动机(Cohen's d=0.59,p=0.003),对学生的深度访谈结果表明,生成式评语能作为考试的一种总结反思工具,体现了教师对学生个性化的关注和鼓励。研究结果为人工智能技术助力个性化、精准化教学提供了研究思路,为教育环境中持续评估和改进生成式人工智能技术的使用提供了建议。

IP属地化政策对舆情评论的影响研究——基于微博用户属地公开的准自然实验

[目的/意义]实证评估公开属地政策能否以及如何对舆情评论产生影响,为网络舆情研究、平台优化和治理提供启示。[研究设计/方法]基于自我监控理论和隐私计算理论构建研究模型和假设,以微博“IP属地”功能的推出为准自然实验,运用双重差分法和断点回归模型探究IP属地化政策对舆情评论的影响。[结论/发现]政策引发用户自我监控效应和风险感知效应,不仅整体改善了评论情感倾向、评论可读性和共情性,也能够显著抑制评论情感极性,该结论在一系列稳健性检验后仍然成立。政策影响在不同用户群体之间存在显著差异,相较处于事件热点中心的用户,属地公开更能促使处于非热点中心的用户做出改变。政策同时加剧评论中的地域歧视现象。[创新/价值]揭示了IP属地化政策对在线评论行为的作用,为清朗网络环境提供新视角,丰富了IP属地化与舆情评论行为影响因素的相关研究。

元宇宙环境下图书馆阅读推广研究:现实意义、基本要素和实施路径

文章梳理元宇宙阅读相关理论研究成果,论述元宇宙环境下图书馆阅读推广的现实意义,探究元宇宙环境下图书馆阅读推广主体、客体、对象三大要素,并提出元宇宙环境下图书馆阅读推广实施路径,即加强人才培育,推动高技能、复合型馆员队伍建设;共创阅读资源,参与特色化、专题性文献内容生产;推进技术应用,优化沉浸式、交互式读者阅读体验;健全保障机制,构建合理、规范的元宇宙图书馆阅读生态。

英国公共图书馆老年人与闭居者服务调查与启示

文章以19个英国公共图书馆为调查对象,采用网站调查法、邮件咨询法和文献调研法,对英国公共图书馆的老年人与闭居者服务内容进行探究。调查发现,英国公共图书馆的老年人与闭居者服务内容主要围绕着健康与医疗服务、无障碍服务、送书上门服务、特惠服务、休闲服务五个方面展开,并通过分析英国公共图书馆面向老年人与闭居者服务的现状、内容和特点,提炼出其对我国公共图书馆开展相关服务的启示。

基于评论挖掘消费者偏好的蓝牙耳机设计策略

目的 本研究旨在从在线评论数据中挖掘消费者偏好信息,以探索蓝牙耳机设计的改良策略。方法 使用Python爬虫抓取目标消费者评论,并通过数据挖掘和文本分析定位需求主题和关键词后,基于消费者偏好理论,对产品功能属性进行情感分析和需求描述以控制变量,运用Mixed-logit模型提取消费者偏好特征以寻求最优的设计策略。结果 通过定量的实证研究,回归分析了偏好特征参数的显著性表达,生成蓝牙耳机改良策略实例并通过满意度检测验证其可行性。结论 结合评论挖掘和Mixed-logit模型,不仅能够设计出以消费者偏好为核心的产品设计方案,还可以为相关产品的设计策略改进提供指导与建议,具有一定的创新性和实用性。

负面评论引导的高维多元数据可视分析系统

随着互联网平台以及多用户社交网络的成熟,群体用户消费体验的参考价值日趋扩大,在海量评论数据中,负面评论对企业和消费者的参考价值更为突出,有效的面向负面评论的可视分析是有必要的.针对评论数据高维多元的特征,为了给企业和消费者提供全新的评论分析视角,以负面评论为切入点,给出负面评论的划定范围,提出了一个交互式的可视分析系统.首先,利用情感分析和意见挖掘方法处理用户评论数据,并提出评论个体影响力差异量化方法;其次设计了主题情感波纹图、评论比较视图等一系列交互式可视化表示方法,利用动态交互式方法构建多维度关联视图探索影响负面评论产生的因素,负面评论产生的原因及其个体化差异.3个案例的结果表明,所提系统是有效和实用的;同时,该系统还可扩展应用于其他领域的评论文本可视分析中.

我国公共图书馆参与开放教育资源运动初探

开放教育资源运动已开展二十余年,高校和研究型图书馆发挥教育职能,积极参与其中。对我国公共图书馆参与开放教育资源运动的可行性进行剖析,包括动机、机会与能力。针对公共图书馆会面临的版权、质量、配套资源等方面的挑战,提出制定政策、联合社会力量、关注基础教育、资源建设与服务、专业人员培训等方面的解决策略。

引用评论证据视角下高水平论文遴选研究

论文作为科研成果的重要表现形式,在“破五唯”的大背景下,科学遴选高水平论文至关重要,是保障代表作评价工作落地的基本抓手。引用评论是学术共同体对论文价值最直接的认可,亦是学术评价的实质性关键证据,对其内容进行深入挖掘有助于更科学地发现高水平论文。首先,本文基于学术认同理论和证据特征阐释了模型构建的基本思想;其次,对基于内容语义的引用情感和引用功能进行重新界定和分类,并充分考虑“施引作者可信度”,构建高水平论文遴选综合加权模型;再其次,选取化学领域顶级刊物Angewandte Chemie-International Edition中的相关论文展开实证研究,结果表明,本文提出的模型可以较精准地筛选出“非常重要论文(very important paper,VIP)”;最后,与其他主流评价指标进行比较分析,证实了本文模型具有较高的区分度和鉴别度,在论文学术水平评价中具有显著优越性。

使用虚拟现实技术提高冰川地貌表达维度的研究

冰川侵蚀与堆积地貌含有重要的古气候、古环境信息,是鉴别冰川作用范围和性质的标志,也是冰川动力学研究的基础。冰川地貌的表达研究经过150多年的发展,已经形成了较完备的冰川地貌制图方案,为冰川形成过程、冰川类型及冰川作用的动力学特征判断提供了重要依据。然而,冰川地貌专题图的专业性为古冰川重建和古环境研究提供支撑的同时,也提高了非专业人士的解读难度。以虚拟现实/增强现实地图、混合现实地图、微地图、语义地图和隐喻地图等的泛地图表达机制提供了更多维度的表达方案,在传统的空间几何维度的基础上增加了状态和读图者视角等维度,为冰川地貌表达提供了新的视角。本文使用虚拟现实(virtual reality,VR)技术构建了卡若拉冰川西沟的交互式三维冰川地貌场景,增加了三维、动态和读图者视角等表达维度。通过前期研究工作基础(沉积物特征、测年等工作)对场景内典型的冰川地貌进行标注,将前期冰川地貌的研究认识与VR场景进行较好的对应。该场景可以720°自由变换,包含末次冰盛期-晚冰期、早全新世、新冰期、小冰期和现代冰川共40个分场景,可以激发读图者的沉浸感、交互感和构想感。VR等技术对冰川地貌进行泛地图式的更多维度表达,可以为冰川地貌教学、科普及学术交流提供良好的交互式三维冰川地貌素材,也可以辅助读者更加准确地判别冰川遗迹和认知冰川发育条件。

从网络评价语料库看中国文学人物形象的重塑效果——以《射雕英雄传》中郭靖人物形象英译为例

金庸的长篇武侠小说《射雕英雄传》塑造了一系列个性化的人物形象,具有“英雄史诗”风格。以“豆瓣读书”和“好读网”上中英文读者对《射雕英雄传》的网络评价语料为基础,自建原文读者和译文读者评价语料库,通过比较中英读者对《射雕英雄传》中郭靖人物形象特征评价的异同,会发现英文读者对郭靖的某些性格特征的评价趋于扁平化,反映了郭靖人物形象在英译本中的重塑效果。英文读者对人物形象中蕴含中国特色的异质文化要素缺乏足够的知识储备,因此,在英译过程中译者应采取语境信息充实的翻译策略,适当为英文读者补充所缺失的语境信息,让读者以此为线索,推理出异质文化所传递的隐含意义,从而使读者对郭靖人物形象的解读更为丰满,以进一步提升翻译质量。