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نتیجه جستجو - توییتر

تعداد مقالات یافته شده: 46
ردیف عنوان نوع
21 Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis
تجزیه و تحلیل احساسات در توییتر: رویکرد معانی متن به بحران پناهندگان سوری-2018
The use of social media has become an integral part of daily routine in modern society. Social media portals offer powerful public platforms where people can freely share their opinions and feelings about various topics with large crowds. In the current study, we investigated the public opinions and sentiments towards the Syrian refugee crisis, which has affected millions of people and has become a widely discussed, polarizing topic in social media around the world. To analyze public sentiments about the topic on Twitter, we collected a total of 2381,297 relevant tweets in two languages including Turkish and English. Turkish sentiments were considered important as Turkey has welcomed the largest number of Syrian refugees and Turkish tweets carried in formation to reflect public perception of a refugee hosting country first handedly. We performed a comparative sentiment analysis of retrieved tweets. The results indicated that the sentiments in Turkish tweets were significantly different from the sentiments in English tweets. We found that Turkish tweets carried slightly more positive sentiments towards Syrians and refugees than neutral and negative sentiments, nevertheless the sentiments of tweets were almost evenly dis tributed among the three major categories. On the other hand, the largest number of English tweets by a significant margin contained neutral sentiments, which was followed by the negative sentiments. In comparison to the ratio of positive sentiments in Turkish tweets, 35% of all Turkish tweets, the proportion of English tweets contained remarkably less positive sentiments towards Syrians and refugees, only 12% of all English tweets.
Keywords: Sentiment analysi ، Text mining ، Big data ، Refugee ، Twitter
مقاله انگلیسی
22 Semantic-based Followee Recommendations on Twitter Network
توصیه های دنباله ای مبتنی بر معنا در شبکه توییتر-2018
Twitter bloggers use the concept of follower/followee in order to inform and to be informed of all recent activities of users who have similar interests and preferences. Moreover, finding relevant users to follow becomes a crucial task due to the rapid growth of Twitter network and the huge number of daily registered users. Thus, the need for a system to assist users in such task is very important. Indeed, recent studies use lexical analysis to recommend people to follow. In this paper, we propose a followee recommender system based on semantic analysis of user profiles content by leveraging the follower/followee topology. We perform experiments using a real dataset harvested from Twitter. Experimental results show that our approach improves lexical based approach by more than 5% on recall value for recommending 5 followees, proving that dealing with semantic gap in microblogging content is more relevant for the quality of recommending like-minded users.
Keywords: recommender system; semantic web; lexical similarity; semantic similarity; followee recommendation
مقاله انگلیسی
23 Mobile Social Big Data: WeChat Moments Dataset, Network Applications, and Opportunities
داده های بزرگ اجتماعی موبایل : مجموعه داده های لحظه ای WeChat، برنامه های شبکه، و فرصت ها-2018
In parallel with the increase of various mobile technologies, the MSN service has brought us into an era of mobile social big data, where people are creating new social data every second and everywhere. It is of vital importance for businesses, governments, and institutions to understand how peoples behaviors in the online cyberspace can affect the underlying computer network, or their offline behaviors at large. To study this problem, we collect a dataset from WeChat Moments, called WeChatNet, which involves 25,133,330 WeChat users with 246,369,415 records of link reposting on their pages. We revisit three network applications based on the data analytics over WeChatNet, i.e., the information dissemination in mobile cellular networks, the network traffic prediction in backbone networks, and the mobile population distribution projection. We also discuss the potential research opportunities for developing new applications using the released dataset.
Keywords: Twitter, Facebook, Business, IP networks, Data analysis, Sociology
مقاله انگلیسی
24 Social media data analytics to improve supply chain management in food industries
تجزیه و تحلیل داده های رسانه های اجتماعی برای بهبود مدیریت زنجیره تامین در صنایع غذایی-2017
This paper proposes a big-data analytics-based approach that considers social media (Twitter) data for the identification of supply chain management issues in food industries. In particular, the proposed approach includes text analysis using a support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included a cluster of words which could inform supply-chain (SC) decision mak ers about customer feedback and issues in the flow/quality of food products. A case study in the beef supply chain was analysed using the proposed approach, where three weeks of data from Twitter were used.
Keywords: Beef supply chain | Twitter data | Sentiment analysis
مقاله انگلیسی
25 Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method
شناسایی گسترش دهنده های نفوذ در شبکه های اجتماعی آنلاین با استفاده از روش تجزیه K-core با تعادل وزن-2017
Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.
Keywords: Online social networks | Complex networks | Influential spreaders | K-shell decomposition | Social media | Twitter
مقاله انگلیسی
26 Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method
شناسایی گسترش نفوذ در شبکه های اجتماعی آنلاین با استفاده از تعامل روش تجزیه وزنی K هسته ای-2017
Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.
Keywords: Online social networks | Complex networks | Influential spreaders | K-shell decomposition | Social media | Twitter
مقاله انگلیسی
27 Privacy concerns on social networking sites: Interplay among posting types, content, and audiences
نگرانی های خصوصی در مورد سایت های شبکه های اجتماعی: تعامل میان انواع پستها، محتوا و مخاطبان-2017
This study examines the impact of the types of posting, information types, and privacy concerns toward audience types across two types of social networking sites (SNSs), Facebook and Twitter. The findings indicate that on Facebook, young SNS users are more concerned about other users posting on their own timeline than other types of posting. On Twitter, young SNS users are more concerned about their own tweets than other users retweeting their tweets. The study also found that different content within different posting types has varying influence on privacy concerns constructed by the user based on three audience types (marketer, authoritative, distant relations). Implications for policy-making and suggestions for future research are discussed.
Keywords: Privacy concerns on social networking sites | (SNS) | Content shared on social media | Posting types | Facebook | Twitter
مقاله انگلیسی
28 Discovering socially important locations of social media users
کشف مکان های اجتماعی مهم از کاربران رسانه های اجتماعی-2017
Socially important locations are places that are frequently visited by social media users in their social media life. Discovering socially interesting, popular or important locations from a location based social network has recently become important for recommender systems, targeted advertisement applications, and urban planning, etc. However, discovering socially important locations from a social network is chal lenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for developing computationally efficient approaches, and the difficulty of modeling human behavior. In the literature, several studies are conducted for discovering socially important locations. However, majority of these studies focused on discovering locations without considering historical data of social media users. They focused on analysis of data of social groups without considering each user’s preferences in these groups. In this study, we proposed a method and interest measures to discover socially important loca tions that consider historical user data and each user’s (individual’s) preferences. The proposed algorithm was compared with a naïve alternative using real-life Twitter dataset. The results showed that the pro posed algorithm outperforms the naïve alternative.
Keywords: Socially important locations mining | Spatial social media mining | Historical social media data analysis | Social media networking sites | Twitter
مقاله انگلیسی
29 What makes you tick? The psychology of social media engagement in space science communication
چه چیزی باعث میشودشماتیک بزنید ؟ روانشناسی تعامل رسانه های اجتماعی در ارتباطات علوم فضایی-2017
The rise of social media has transformed the way the public engages with science organisations and scientists. ‘Retweet’, ‘Like’, ‘Share’ and ‘Comment’ are a few ways users engage with messages on Twitter and Facebook, two of the most popular social media platforms. Despite the availability of big data from these digital footprints, research into social media science communication is scant. This paper presents a novel empirical study into the features of engaging science-related social media messages, focusing on space science communications. It is hypothesised that these messages contain certain psycholinguistic features that are unique to the field of space science. We built a predictive model to forecast the engagement levels of social media posts. By using four feature sets (n-grams, psycholinguistics, grammar and social media), we were able to achieve prediction accuracies in the vicinity of 90% using three su pervised learning algorithms (Naive Bayes, linear classifier and decision tree). We conducted the same experiments on social media messages from three other fields (politics, business and non-profit) and discovered several features that are exclusive to space science communications: anger, authenticity, hashtags, visual descriptionsdbe it visual perception-related words, or media elementsdand a tentative tone.
Keywords: Science communication | Social media | Machine learning | Psychometrics | Facebook | Twitter
مقاله انگلیسی
30 Influence of social networks on congresses of urological societies and associations: Results of the 81st National Congress of the Spanish Urological Association
تأثیر شبکه های اجتماعی در کنگره جوامع و انجمن های ارولوژی: نتایج کنگره ملی 81 ام در انجمن ارولوژی اسپانیا-2017
Objective: To measure social network activity during the 81st National Congress of the Spanish Urological Association (AEU) and to compare it with the activity during other congresses of national and international urological associations. Material and methods: We designed and registered the official hashtag #AEU16 for the 81st National Congress of the AEU on the Symplur website. The following measurements were recorded: number of participants, number of tweets, tweets by participant, tweets per hour and views. Results: The number of participants in the social network activity during the congress was 207. The measurements of activity in Twitter consisted of a total of 1866 tweets, a mean rate of 16 tweets/h, 9 tweets per participant and 1,511,142 views. The activity during the interna tional congresses is as follows: 2016 American Urological Association annual congress (views: 28,052,558), 2016 European Association of Urology annual congress (views: 13,915,994), 2016 Urological Society of Australia and New Zealand (views: 4,757,453), 2015 Société Internationale d’Urologie annual congress (views: 1,023,038). The activity during the national congresses was recorded as follows: 2016 Annual Conference of The British Association of Urological Surgeons (views: 2,518,880), 81st National Congress of the AEU (views: 1,511,142), 109th Congress of l’Association Franc ¸aise d’Urologie (views: 662,828), 67th German Congress of Urology (views: 167,347). We found 10 posts in Facebook and 2 communications via Periscope TV related to #AEU16. Conclusions: The social network activity during the 81st National Congress of the AEU was notable given the results of this study. The use of social networks has expanded among urological associations, congresses and meetings, giving them a global character.
KEYWORDS: Social networks | Twitter | Periscope | Congress | Urology
مقاله انگلیسی
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