This mixed study aims to highlight the impact of social media in the Arab world, specifically Twitter’s impact on translators’ communities. For this purpose, the role of hashtags among translators will be examined b...This mixed study aims to highlight the impact of social media in the Arab world, specifically Twitter’s impact on translators’ communities. For this purpose, the role of hashtags among translators will be examined by investigating one particular Arabic hashtag, its purpose, target users, and the classification of content. The hashtag is , #translator_serving_translator. 1) An online survey of six closed questions was employed and posted on Twitter, and 249 responses show that users are from fourteen Arab countries, and the majority is from Saudi Arabia. Hashtag users are translators, freelancers, or TS students. Some are active users who post tweets and answer questions, others only ask questions, and the rest only read tweets. The general attitude toward employing hashtags among translators’ communities was positive. 2) Employing a content analysis approach, the content is classified into two main categories of sharing information and seeking assistance with seven subcategories of each.展开更多
Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and ana...Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and analysis of these tagless microblogs. In this paper, we summarize the similarity between microblogs and shortmessage-style news, and then propose an algorithm, named 5WTAG, for detecting microblog topics based on a model of five Ws（When, Where, Who, What, ho W）. As five-W attributes are the core components in event description, it is guaranteed theoretically that 5WTAG can properly extract semantic topics from microblogs. We introduce the detailed procedure of the algorithm in this paper including spam microblog identification, microblog segmentation, and candidate hashtag construction. In addition, we propose a novel recommendation computing method for ranking candidate hashtags, which combines syntax and semantic analysis and observes the distribution of artificial topic hashtags. Finally, we conduct comprehensive experiments to verify the semantic correctness and completeness of the candidate hashtags, as well as the accuracy of the recommendation method using real data from Sina Weibo.展开更多
Purpose: In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society(beyond science). We address the following question: can Twitter be meaningf...Purpose: In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society(beyond science). We address the following question: can Twitter be meaningfully used for the mapping of public and scientific discourses?Design/methodology/approach: Recently, Haunschild et al.(2019) introduced a new network-oriented approach for using Twitter data in research evaluation. Such a procedure can be used to measure the public discussion around a specific field or topic. In this study, we used all papers published in the Web of Science(WoS, Clarivate Analytics) subject category Information Science & Library Science to explore the publicly discussed topics from the area of library and information science(LIS) in comparison to the topics used by scholars in their publications in this area.Findings: The results show that LIS papers are represented rather well on Twitter. Similar topics appear in the networks of author keywords of all LIS papers, not tweeted LIS papers, and tweeted LIS papers. The networks of the author keywords of all LIS papers and not tweeted LIS papers are most similar to each other.Research limitations: Only papers published since 2011 with DOI were analyzed.Practical implications: Although Twitter data do not seem to be useful for quantitative research evaluation, it seems that Twitter data can be used in a more qualitative way for mapping of public and scientific discourses.Originality/value: This study explores a rather new methodology for comparing public and scientific discourses.展开更多
On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend ha...On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend hashtags for tweets has received wide attention.The previous hashtag recommendation methods were to convert the task into a multi-class classification problem.However,these methods can only recommend hashtags that appeared in historical information,and cannot recommend the new ones.In this work,we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task.To train and evaluate the proposed method,we used the real tweet data which is collected from Twitter.Experimental results show that the proposed method can be significantly better than the most advanced method.Compared with the state-of-the-art methods,the accuracy of our method has been increased 4%.展开更多
This paper provides an auto-ethnographic observation of hashtag feminist activism on Weibo, setting in a context of post-feminism age in China. Two subjects, the Hot Search List and its Public Discussion Forum, were c...This paper provides an auto-ethnographic observation of hashtag feminist activism on Weibo, setting in a context of post-feminism age in China. Two subjects, the Hot Search List and its Public Discussion Forum, were chose to examine the complexity of the current situation of this hashtag activism. An auto-ethnographic methodology was used to interrogate the states quo of Chinese online feminist movement, revealing gender-centric discussions reinforcing stereotypes under the guise of equality. Misogynistic narratives, algorithmic constraints, censorship, and official opposition pose significant barriers to feminist discourse. Nonetheless, the study identifies a potential for hashtag activism within Weibo’s discourse, offering a space for resistance. By acknowledging these challenges, this paper seeks to empower Chinese feminists to challenge dominant narratives and advocate for their rights.展开更多
Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are inte...Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommenda- tion considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue spe- cially to recommend personalized hashtags combining long- term and short-term user interest. We introduce three features to capture personal and temporal user interest： 1） hashtag textual information; 2） user behavior; and 3） time. We of- fer two recommendation models for comparison： a linear- combined model, and an enhanced session-based temporal graph （STG） model, Topic-STG, considering the features to learn user preferences and subsequently recommend person- alized hashtags. Experiments on two real tweet datasets illus- trate the effectiveness of the proposed models and algorithms.展开更多
Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to th...Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features （i.e., user, hashtag, text） of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.展开更多
文摘This mixed study aims to highlight the impact of social media in the Arab world, specifically Twitter’s impact on translators’ communities. For this purpose, the role of hashtags among translators will be examined by investigating one particular Arabic hashtag, its purpose, target users, and the classification of content. The hashtag is , #translator_serving_translator. 1) An online survey of six closed questions was employed and posted on Twitter, and 249 responses show that users are from fourteen Arab countries, and the majority is from Saudi Arabia. Hashtag users are translators, freelancers, or TS students. Some are active users who post tweets and answer questions, others only ask questions, and the rest only read tweets. The general attitude toward employing hashtags among translators’ communities was positive. 2) Employing a content analysis approach, the content is classified into two main categories of sharing information and seeking assistance with seven subcategories of each.
基金supported by the National Natural Science Foundation of China (No. 61173027)the Northeastern University Fundamental Research Funds for the Central Universities (Nos. N150404012 and N140404006)
文摘Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and analysis of these tagless microblogs. In this paper, we summarize the similarity between microblogs and shortmessage-style news, and then propose an algorithm, named 5WTAG, for detecting microblog topics based on a model of five Ws（When, Where, Who, What, ho W）. As five-W attributes are the core components in event description, it is guaranteed theoretically that 5WTAG can properly extract semantic topics from microblogs. We introduce the detailed procedure of the algorithm in this paper including spam microblog identification, microblog segmentation, and candidate hashtag construction. In addition, we propose a novel recommendation computing method for ranking candidate hashtags, which combines syntax and semantic analysis and observes the distribution of artificial topic hashtags. Finally, we conduct comprehensive experiments to verify the semantic correctness and completeness of the candidate hashtags, as well as the accuracy of the recommendation method using real data from Sina Weibo.
文摘Purpose: In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society(beyond science). We address the following question: can Twitter be meaningfully used for the mapping of public and scientific discourses?Design/methodology/approach: Recently, Haunschild et al.(2019) introduced a new network-oriented approach for using Twitter data in research evaluation. Such a procedure can be used to measure the public discussion around a specific field or topic. In this study, we used all papers published in the Web of Science(WoS, Clarivate Analytics) subject category Information Science & Library Science to explore the publicly discussed topics from the area of library and information science(LIS) in comparison to the topics used by scholars in their publications in this area.Findings: The results show that LIS papers are represented rather well on Twitter. Similar topics appear in the networks of author keywords of all LIS papers, not tweeted LIS papers, and tweeted LIS papers. The networks of the author keywords of all LIS papers and not tweeted LIS papers are most similar to each other.Research limitations: Only papers published since 2011 with DOI were analyzed.Practical implications: Although Twitter data do not seem to be useful for quantitative research evaluation, it seems that Twitter data can be used in a more qualitative way for mapping of public and scientific discourses.Originality/value: This study explores a rather new methodology for comparing public and scientific discourses.
文摘On Twitter,people often use hashtags to mark the subject of a tweet.Tweets have specific themes or content that are easy for people to manage.With the increase in the number of tweets,how to automatically recommend hashtags for tweets has received wide attention.The previous hashtag recommendation methods were to convert the task into a multi-class classification problem.However,these methods can only recommend hashtags that appeared in historical information,and cannot recommend the new ones.In this work,we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task.To train and evaluate the proposed method,we used the real tweet data which is collected from Twitter.Experimental results show that the proposed method can be significantly better than the most advanced method.Compared with the state-of-the-art methods,the accuracy of our method has been increased 4%.
文摘This paper provides an auto-ethnographic observation of hashtag feminist activism on Weibo, setting in a context of post-feminism age in China. Two subjects, the Hot Search List and its Public Discussion Forum, were chose to examine the complexity of the current situation of this hashtag activism. An auto-ethnographic methodology was used to interrogate the states quo of Chinese online feminist movement, revealing gender-centric discussions reinforcing stereotypes under the guise of equality. Misogynistic narratives, algorithmic constraints, censorship, and official opposition pose significant barriers to feminist discourse. Nonetheless, the study identifies a potential for hashtag activism within Weibo’s discourse, offering a space for resistance. By acknowledging these challenges, this paper seeks to empower Chinese feminists to challenge dominant narratives and advocate for their rights.
文摘Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommenda- tion considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue spe- cially to recommend personalized hashtags combining long- term and short-term user interest. We introduce three features to capture personal and temporal user interest： 1） hashtag textual information; 2） user behavior; and 3） time. We of- fer two recommendation models for comparison： a linear- combined model, and an enhanced session-based temporal graph （STG） model, Topic-STG, considering the features to learn user preferences and subsequently recommend person- alized hashtags. Experiments on two real tweet datasets illus- trate the effectiveness of the proposed models and algorithms.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61320106006, 61532006, 61772083, and 61502042, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2017RC39.
文摘Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features （i.e., user, hashtag, text） of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.
