با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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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 |