Twitter

摘要： 随着互联网的飞速发展,人们的生活获得了极大的便捷,但同时网络平台也滋生了大量的虚假信息,任由虚假的网络谣言广泛传播会极大地影响人们的生活,甚至影响国家的秩序,因此谣言检测研究具有较高的意义。文章对谣言检测方法的相关文献进行归纳分析,将检测方法按照谣言的特点分成了四类:基于内容的方法、基于用户信息的方法、基于传播的方法、基于外部知识引入的方法。然后介绍了部分公开的谣言检测数据集。最后对全文进行总结并展望了谣言检测的发展方向。

摘要： Purpose:Social media users share their ideas,thoughts,and emotions with other users.However,it is not clear how online users would respond to new re search outcomes.This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications.Additionally,we investigate what features of the research articles help in such prediction.Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.Design/methodology/appro ach:Several tools are used for sentiment analysis,so we applied five sentiment analysis tools to check which are suitable for capturing a tweet’s sentiment value and decided to use NLTK VADER and TextBlob.We segregated the sentiment value into negative,positive,and neutral.We measure the mean and median of tweets’sentiment value for research articles with more than one tweet.We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models.Findings:We found that the most important feature in all the models was the sentiment of the research article title followed by the author count.We observed that the tree-based models performed better than other classification models,with Random Forest achieving 89%accuracy for binary clas sification and 73%accuracy for three-label clas sification.Research limitations:In this research,we used state-of-the-art sentiment analysis libraries.However,these libraries might vary at times in their sentiment prediction behavior.Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper’s details.In the future,we intend to broaden the scope of our research by employing word2 vec models.Practical implications:Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes.Research in this area has relied on fewer and more limited measures,such as citations and user studies with small datasets.There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research.This study will help scientists better comprehend the emotional impact of their work.Additionally,the value of understanding the public’s interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public.Originality/value:This study will extend work on public engagement with science,sociology of science,and computational social science.It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

摘要： In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media.Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels.Therefore,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet sentiments.This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and Ethereum.The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

摘要： n the age of the internet,social media are connecting us all at the tip of our fingers.People are linkedthrough different social media.The social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights.This paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman Urdu.Explore machine learning solutions for sentiment analysis and train models,collect the data on government from Twitter,apply sentiment analysis,and provide a python library that classifies text sentiment.Training data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:11k.Five different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is explored.The Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%accuracy.Lastly,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy.

摘要： Depression has been a major global concern for a long time,with the disease affecting aspects of many people’s daily lives,such as their moods,eating habits,and social interactions.In Arabic culture,there is a lack of awareness regarding the importance of facing and curing mental health diseases.However,people all over the world,including Arab citizens,tend to express their feelings openly on social media,especially Twitter,as it is a platform designed to enable the expression of emotions through short texts,pictures,or videos.Users are inclined to treat their Twitter accounts as diaries because the platform affords them anonymity.Many published studies have detected the occurrence of depression among Twitter users on the basis of data on tweets posted in English,but research on Arabic tweets is lacking.The aim of the present work was to develop a model for analyzing Arabic users’tweets and detecting depression among Arabic Twitter users.And expand the diversity of user tweets,by adding a new label(“neutral”)so the dataset include three classes(“depressed”,“non-depressed”,“neutral”).The model was created using machine learning classifiers and natural language processing techniques,such as Support Vector Machine(SVM),Random Forest(RF),Logistic Regression(LR),K-nearest Neighbors(KNN),AdaBoost,and Naïve Bayes(NB).The results showed that the RF classifier outperformed the others,registering an accuracy of 82.39%.

摘要： Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

摘要： Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

摘要： [目的/意义]以Twitter为例,了解社交媒体对数字人文的影响,为如何利用社交媒体平台促进数字人文研究、实践、推广和发展提出建议。[方法/过程]利用Python爬取Twitter中有关数字人文的账户数据和博客数据,对数字人文核心用户进行识别和分类;通过博客内容统计,分析用户使用社交媒体的行为和动机;通过博客标签词统计,归纳数字人文相关研究内容。[结果/结论]社交媒体对数字人文的推动性影响表现在社交媒体是数字人文的虚拟学术社区、社交媒体促进数字人文跨学科学术交流、社交媒体是数字人文的推广平台、社交媒体为数字人文研究提供元数据;限制性影响表现在社交媒体平台的混乱无序性、社交媒体使用的平等问题、社交媒体内容的版权问题。

摘要： 互联网时代,网络呈现出“过度记忆”的特点,用户的任何言行举止都会在网络中留下痕迹。被遗忘权的出现在一定程度上解决了这一问题,使得用户拥有了删除网络痕迹的权利。但是,社交媒体拥有海量用户,它不仅是数据信息的聚集地,更与人们的日常生活高度融合和交汇。鉴于此,本文从被遗忘权本身出发,探讨了其在社交媒体中所面临的困境,并对其在社交媒体中是否可行、是否适用以及如何更好地运用进行了具体分析。

摘要： Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),which is known as Long COVID.Social media platforms like Facebook and Twitter are the primary sources to gather and examine people’s opinion and sentiments towards various topics.Methods:In this paper,we aimed to examine sentiments,discover key themes and associated topics in Long COVID-related messages posted by Twitter users in the US between March 2022 and April 2022 using sentiment analysis and topic modeling.Results:A total of 117,789 tweets were examined,of which three dominant themes were identified,ranging from symptoms to social and economic impacts,and preventive measures.We also found that more negative sentiments were expressed in the tweets by users toward long-term COVID-19.Conclusions:Our research throws light on dominant themes,topics and sentiments surrounding the ongoing public health crisis.From the insights gained,we discuss the major implications of this study for health practitioners and policymakers.

摘要： 本文以Twitter的内涵及特点为研究的基础,阐述了Twitter在远程学习中促进协作探究学习的优点,有目的地在徐州师范大学现代教育技术学教育硕士中进行了基于Twitter的专业英语主题式协作探究学习的实验,分析了实验过程及结果,并且总结出基于Twitter的主题式远程协作探究学习活动的优点及存在的问题.

摘要： 本文以Twitter的内涵及特点为研究的基础,阐述了Twitter在远程学习中促进协作探究学习的优点,有目的地在徐州师范大学现代教育技术学教育硕士中进行了基于Twitter的专业英语主题式协作探究学习的实验,分析了实验过程及结果,并且总结出基于Twitter的主题式远程协作探究学习活动的优点及存在的问题.

摘要： 本文以Twitter的内涵及特点为研究的基础,阐述了Twitter在远程学习中促进协作探究学习的优点,有目的地在徐州师范大学现代教育技术学教育硕士中进行了基于Twitter的专业英语主题式协作探究学习的实验,分析了实验过程及结果,并且总结出基于Twitter的主题式远程协作探究学习活动的优点及存在的问题.

摘要： 本文以Twitter的内涵及特点为研究的基础,阐述了Twitter在远程学习中促进协作探究学习的优点,有目的地在徐州师范大学现代教育技术学教育硕士中进行了基于Twitter的专业英语主题式协作探究学习的实验,分析了实验过程及结果,并且总结出基于Twitter的主题式远程协作探究学习活动的优点及存在的问题.

摘要： 本发明涉及一种基于Twitter数据的社交网络组织成员识别方法，其特征在于，包括以下步骤：爬取关注目标组织公共账号的用户，构成候选集合；寻找确定属于目标组织的用户构成种子集合；设定得分阈值和期望值；爬取候选集合中的每个用户的关注列表与被关注列表、所有推文并提取其中的推送关系；对于候选集合中的每一个用户，根据种子集合，计算识别因子，使用评估模型计算每个用户的得分；根据给定的得分阈值从候选集中筛选出得分大于得分阈值的用户构成结果集合；从候选集合中移除本轮产生的结果集合，并将新产生的结果集合加入种子集合中；迭代执行直到种子集合中成员的数量达到期望值。本发明涉及的方法能够挖掘目标组织中的成员，挖掘准确率高。

摘要： 本发明公开了一种基于多因数@优化模型的Twitter网络信息推送最大化方法，首先综合考虑兴趣相似度与转推意愿二个因素确定目标用户群，随后，将Twitter中的提及@问题转换为线性规划问题，根据用户使用Twitter的实际情况，将用户一天内的发帖模式根据小时划分为24个时间窗口，计算Twitter用户在每个时间窗口的上线概率，并同时考虑了用户在一天24小时内的时序模式和持续一段时间内的整体时序模式，通过将不平衡指派问题转换为平衡指派问题，采用匈牙利算法来解决该指派问题。采用本方法，使得我方账号能够在发布大量帖子的基础上，能够提及@充足的用户，且保障提及@到的用户尽可能及时的接受我方账号的观点。

摘要： 本发明公开了一种针对Twitter的分析取证方法，其包括：获得运行Twitter的智能终端的最高管理权；据此提取Twitter在智能终端中运行的数据信息；进行数据分析，取证Twitter用户的账号信息、用户关注的联系人账号和发布的消息，以及用户与好友的私信内容和用户的搜素历史记录。本发明提供的取证方案，完善了处理智能终端的手段，提高了取证的速度，对Twitter应用中的关注对象、好友、通信信息和搜索历史进行了分析，可以将账号、好友和通信信息进行关联，并且支持对通信消息中发送的图片进行查看。再者本方案支持Android和iOS系统的取证，取证内容多。

摘要： 一种面向Twitter的社交广告可投放性分析方法。包括：创新地结合Twitter用户多种不同来源的语料信息，构建多源Twitter语料库，有效扩充Twitter短文本，便于推断用户发布内容的潜在广告价值，以实现精准广告受众定位；借鉴LDA模型思想，提出多源Twitter语料主题分析模型，对用户发布内容进行隐含语义分析；基于语义分析结果，设计特征选择、过滤和表示算法，构建逻辑回归分类器，关于广告可投放性进行分类，作为广告推荐的决策依据。本发明充分利用用户发布信息特点，能够准确推断其潜在广告价值。通过本发明可得到符合用户真实意图的推断结果。本发明可用于Twitter等社交网络下的广告推荐等领域。

摘要： 本发明公开了一种基于最大团发现的特定Twitter用户群体的挖掘方法，包括以下步骤：S1、基于最大团的推文集合筛选，并获取用户群，进行用户群体挖掘；S2、获取用户群体后的相关分析。本发明提出一种根据用户所传播信息的关系进行用户群体挖掘方法，对一系列信息传播过程进行相关网络构建，并提取其中相关性最高的信息传播集合，再选择其中参与信息传播的用户群体，使用该方法能够较为快速地获取发送相同信息的用户群体。本发明还可以很大程度地降低特定Twitter用户群体挖掘的算法复杂度，缩小用户群体挖掘需要遍历的用户数目，提高求解问题的速度。

摘要： 本发明公开了一种基于多因数@优化模型的Twitter网络信息推送最大化方法，首先综合考虑兴趣相似度与转推意愿二个因素确定目标用户群，随后，将Twitter中的提及@问题转换为线性规划问题，根据用户使用Twitter的实际情况，将用户一天内的发帖模式根据小时划分为24个时间窗口，计算Twitter用户在每个时间窗口的上线概率，并同时考虑了用户在一天24小时内的时序模式和持续一段时间内的整体时序模式，通过将不平衡指派问题转换为平衡指派问题，采用匈牙利算法来解决该指派问题。采用本方法，使得我方账号能够在发布大量帖子的基础上，能够提及@充足的用户，且保障提及@到的用户尽可能及时的接受我方账号的观点。

摘要： 本发明涉及一种基于Twitter数据的社交网络组织成员识别方法，其特征在于，包括以下步骤：爬取关注目标组织公共账号的用户，构成候选集合；寻找确定属于目标组织的用户构成种子集合；设定得分阈值和期望值；爬取候选集合中的每个用户的关注列表与被关注列表、所有推文并提取其中的推送关系；对于候选集合中的每一个用户，根据种子集合，计算识别因子，使用评估模型计算每个用户的得分；根据给定的得分阈值从候选集中筛选出得分大于得分阈值的用户构成结果集合；从候选集合中移除本轮产生的结果集合，并将新产生的结果集合加入种子集合中；迭代执行直到种子集合中成员的数量达到期望值。本发明涉及的方法能够挖掘目标组织中的成员，挖掘准确率高。

摘要： 本发明公开了一种针对Twitter的分析取证方法，其包括：获得运行Twitter的智能终端的最高管理权；据此提取Twitter在智能终端中运行的数据信息；进行数据分析，取证Twitter用户的账号信息、用户关注的联系人账号和发布的消息，以及用户与好友的私信内容和用户的搜素历史记录。本发明提供的取证方案，完善了处理智能终端的手段，提高了取证的速度，对Twitter应用中的关注对象、好友、通信信息和搜索历史进行了分析，可以将账号、好友和通信信息进行关联，并且支持对通信消息中发送的图片进行查看。再者本方案支持Android和iOS系统的取证，取证内容多。

摘要： 本发明提供了一种面向Twitter观点分类的情感增强词嵌入学习方法，涉及计算机技术领域，当同时建模词级别n‑gram和极性信息时，本发明的方法不仅建模tweet文档级别的情感极性信息，还集成了词级别情感信息，并自然地将词级别输入卷积后作为tweet级别的输入。当使用学习到的词嵌入到Twitter观点极性分类任务中，在标准的数据集上的实验结果表明本发明的方法胜过目前的同类方法。

摘要： 本发明涉及一种针对twitter的热点事件爆发预测的方法，用于预测twitter中的某个热点事件是否会造成大规模传播，具体技术方案为，首先通过twitter官方API接收实时推文消息。推文中包含了一个或多个话题，依据话题对推文分类并形成数据集，话题和推文之间的关系为多对多关系。之后将推文中的转发关系和指定转发关系进行梳理并形成数据集。根据需要预测的某个话题将所需的推文整合并组建用户关系网，并将网络分解表达。最终使用分解后的网络预测。该方法相较于传统的情感分析、用户分析方法，能够从全网的用户关系网构成角度出发，全面了解事件网络状态以及波及用户范围，能够及时快速知道当前事件发展趋势，给出预测结果。