GiantSteps is an EU-funded project that aims at developing the next generation of music composition tools for the creative industries by bridging the gap between music information research and end users' requirements. An important component of the project is the extraction of musical and application-targeted knowledge from social media and web resources. In this paper, we sketch potential ways to exploit social media and web data for the tasks of music analysis, creation, and algorithm evaluation.
{"title":"The use of social media for music analysis and creation within the giantsteps project","authors":"Peter Knees","doi":"10.1145/2632188.2632212","DOIUrl":"https://doi.org/10.1145/2632188.2632212","url":null,"abstract":"GiantSteps is an EU-funded project that aims at developing the next generation of music composition tools for the creative industries by bridging the gap between music information research and end users' requirements. An important component of the project is the extraction of musical and application-targeted knowledge from social media and web resources. In this paper, we sketch potential ways to exploit social media and web data for the tasks of music analysis, creation, and algorithm evaluation.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. We collect users' music listening behavior from Twitter using music-related hashtags (e.g., #nowplaying). We then build a predictive model to forecast the Billboard rankings and hit music. The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits. This research suggests that users' music listening behavior on Twitter is highly correlated with general music trends and could play an important role in understanding consumers' music consumption patterns. In addition, we believe that Twitter users' music listening behavior can be applied in the field of Music Information Retrieval (MIR).
{"title":"#nowplaying the future billboard: mining music listening behaviors of twitter users for hit song prediction","authors":"Yekyung Kim, B. Suh, Kyogu Lee","doi":"10.1145/2632188.2632206","DOIUrl":"https://doi.org/10.1145/2632188.2632206","url":null,"abstract":"Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. We collect users' music listening behavior from Twitter using music-related hashtags (e.g., #nowplaying). We then build a predictive model to forecast the Billboard rankings and hit music. The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits. This research suggests that users' music listening behavior on Twitter is highly correlated with general music trends and could play an important role in understanding consumers' music consumption patterns. In addition, we believe that Twitter users' music listening behavior can be applied in the field of Music Information Retrieval (MIR).","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examine the use of clustering to identify selfies in a social media user's photos. Faces are first detected within a user's photos followed by clustering using visual similarity. We define a cluster scoring scheme that uses a combination of within-cluster visual similarity and average face size in a cluster to rank potential selfie-clusters. Finally, we evaluate this ranking approach over a collection of Twitter users and discuss methods that can be used for improving performance in the future. An application of user selfies is estimating demographic information such as age, gender, and race in a more robust fashion.
{"title":"Finding selfies of users in microblogged photos","authors":"D. Joshi, Francine Chen, L. Wilcox","doi":"10.1145/2632188.2632209","DOIUrl":"https://doi.org/10.1145/2632188.2632209","url":null,"abstract":"We examine the use of clustering to identify selfies in a social media user's photos. Faces are first detected within a user's photos followed by clustering using visual similarity. We define a cluster scoring scheme that uses a combination of within-cluster visual similarity and average face size in a cluster to rank potential selfie-clusters. Finally, we evaluate this ranking approach over a collection of Twitter users and discuss methods that can be used for improving performance in the future. An application of user selfies is estimating demographic information such as age, gender, and race in a more robust fashion.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129161604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is our great pleasure to welcome you to the SoMeRA 2014: International Workshop on Social Media Retrieval and Analysis, co-located with SIGIR 2014 in Gold Coast, Australia. The amount of user-generated data (including content and contextual information of the users) has been spiraling during the past few years. Social media are fundamentally changing the way how we communicate. Nowadays, people create, share, and consume a huge number of multimedia material on the web and in particular on social platforms. The faster the growth of these corpora, the harder it gets for the individual to find the media documents which satisfy a particular information need. When it comes to multimedia material in particular, the users might also exhibit an entertainment need, which may involve aspects of novelty, serendipity, familiarity, or popularity. However, current retrieval, recommendation, and browsing techniques often fall short to deal with user-generated data of various kinds (audio, image, video, text, contextual, etc.), especially on a larger scale. Satisfying the information- or entertainment need of users in social media data requires a comprehensive understanding of them, which can be gained to some extent by means of social media analysis and -mining. Corresponding user models which are built from this knowledge will improve retrieval and recommendation in social media, going far beyond text-based search which is still the most common paradigm. The gained knowledge also enables intelligently informed and enriched applications in various media domains. The purpose of SoMeRA 2014 is to bring together researchers of different domains who are involved in social media analysis, mining, and retrieval, for instance, experts in multimedia, recommender systems, and user modeling. This is reflected by the 19 submissions received that cover topics as diverse as multimedia retrieval and exploration, user-aware recommender systems, network analysis, event detection, and computational linguistics in social media. Out of these, we selected the most outstanding works to be presented at the workshop, which features 5 oral and 8 poster presentations. In addition, the program includes a keynote speech by Prof. Tat-Seng Chua, National University of Singapore, entitled "From Social Media Data to Actionable Analytics".
{"title":"Proceedings of the first international workshop on Social media retrieval and analysis","authors":"M. Schedl, Peter Knees, Jialie Shen","doi":"10.1145/2632188","DOIUrl":"https://doi.org/10.1145/2632188","url":null,"abstract":"It is our great pleasure to welcome you to the SoMeRA 2014: International Workshop on Social Media Retrieval and Analysis, co-located with SIGIR 2014 in Gold Coast, Australia. \u0000 \u0000The amount of user-generated data (including content and contextual information of the users) has been spiraling during the past few years. Social media are fundamentally changing the way how we communicate. Nowadays, people create, share, and consume a huge number of multimedia material on the web and in particular on social platforms. The faster the growth of these corpora, the harder it gets for the individual to find the media documents which satisfy a particular information need. When it comes to multimedia material in particular, the users might also exhibit an entertainment need, which may involve aspects of novelty, serendipity, familiarity, or popularity. However, current retrieval, recommendation, and browsing techniques often fall short to deal with user-generated data of various kinds (audio, image, video, text, contextual, etc.), especially on a larger scale. Satisfying the information- or entertainment need of users in social media data requires a comprehensive understanding of them, which can be gained to some extent by means of social media analysis and -mining. Corresponding user models which are built from this knowledge will improve retrieval and recommendation in social media, going far beyond text-based search which is still the most common paradigm. The gained knowledge also enables intelligently informed and enriched applications in various media domains. \u0000 \u0000The purpose of SoMeRA 2014 is to bring together researchers of different domains who are involved in social media analysis, mining, and retrieval, for instance, experts in multimedia, recommender systems, and user modeling. This is reflected by the 19 submissions received that cover topics as diverse as multimedia retrieval and exploration, user-aware recommender systems, network analysis, event detection, and computational linguistics in social media. Out of these, we selected the most outstanding works to be presented at the workshop, which features 5 oral and 8 poster presentations. In addition, the program includes a keynote speech by Prof. Tat-Seng Chua, National University of Singapore, entitled \"From Social Media Data to Actionable Analytics\".","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the ongoing EU-FP7 project "Performances as Highly Enriched aNd Interactive Concert eXperiences" (PHENICX), one aim is to make Classical music appealing to new audiences, not at least the typically younger generation of social media users. In the context of the "Social Media Retrieval and Analysis" (SoMeRA) workshop, this paper sheds light on the use of two social media platforms (Last.fm and Twitter) by fans of Classical music.
{"title":"Social media and classical music?: a first analysis within the PHENICX project: \"performances as highly enriched aNd interactive concert eXperiences\"","authors":"M. Schedl","doi":"10.1145/2632188.2632213","DOIUrl":"https://doi.org/10.1145/2632188.2632213","url":null,"abstract":"In the ongoing EU-FP7 project \"Performances as Highly Enriched aNd Interactive Concert eXperiences\" (PHENICX), one aim is to make Classical music appealing to new audiences, not at least the typically younger generation of social media users. In the context of the \"Social Media Retrieval and Analysis\" (SoMeRA) workshop, this paper sheds light on the use of two social media platforms (Last.fm and Twitter) by fans of Classical music.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123779853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alejandro Metke-Jimenez, Sarvnaz Karimi, Cécile Paris
The discovery of suspected adverse drug reactions is no longer restricted to mining reports that pharmaceutical companies and health professionals send to regulators for possible safety signals. Patient forums and other social media are being studied for additional sources of information to assist in expediting adverse reaction discovery. Extracting information on drugs, adverse drug reactions, diseases and symptoms, or patient demographics from such media is an essential step of this process, but it is not straightforward. While most studies in this area use a lexicon-based information extraction methodology, they do not explicitly evaluate the impact of text-processing steps on their final results. We experimentally quantify the value of the most popular techniques to establish whether or not they benefit the information extraction process.
{"title":"Evaluation of text-processing algorithms for adverse drug event extraction from social media","authors":"Alejandro Metke-Jimenez, Sarvnaz Karimi, Cécile Paris","doi":"10.1145/2632188.2632200","DOIUrl":"https://doi.org/10.1145/2632188.2632200","url":null,"abstract":"The discovery of suspected adverse drug reactions is no longer restricted to mining reports that pharmaceutical companies and health professionals send to regulators for possible safety signals. Patient forums and other social media are being studied for additional sources of information to assist in expediting adverse reaction discovery. Extracting information on drugs, adverse drug reactions, diseases and symptoms, or patient demographics from such media is an essential step of this process, but it is not straightforward. While most studies in this area use a lexicon-based information extraction methodology, they do not explicitly evaluate the impact of text-processing steps on their final results. We experimentally quantify the value of the most popular techniques to establish whether or not they benefit the information extraction process.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133454136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefano Mizzaro, M. Pavan, Ivan Scagnetto, Martino Valenti
We address the problem of the categorization of short texts, like those posted by users on social networks and microblogging platforms. We specifically focus on Twitter. Since short texts do not provide sufficient word occurrences, and they often contain abbreviations and acronyms, traditional classification methods such as "Bag-of-Words" have limitations. Our proposed method enriches the original text with a new set of words, to add more semantic value by using information extracted from webpages of the same temporal context. Then we use those words to query Wikipedia, as an external knowledge base, with the final goal to categorize the original text using a predefined set of Wikipedia categories. We also present a first experimental evaluation that confirms the effectiveness of the algorithm design and implementation choices, highlighting some critical issues with short texts.
{"title":"Short text categorization exploiting contextual enrichment and external knowledge","authors":"Stefano Mizzaro, M. Pavan, Ivan Scagnetto, Martino Valenti","doi":"10.1145/2632188.2632205","DOIUrl":"https://doi.org/10.1145/2632188.2632205","url":null,"abstract":"We address the problem of the categorization of short texts, like those posted by users on social networks and microblogging platforms. We specifically focus on Twitter. Since short texts do not provide sufficient word occurrences, and they often contain abbreviations and acronyms, traditional classification methods such as \"Bag-of-Words\" have limitations. Our proposed method enriches the original text with a new set of words, to add more semantic value by using information extracted from webpages of the same temporal context. Then we use those words to query Wikipedia, as an external knowledge base, with the final goal to categorize the original text using a predefined set of Wikipedia categories. We also present a first experimental evaluation that confirms the effectiveness of the algorithm design and implementation choices, highlighting some critical issues with short texts.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129314417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for AD classification using probabilistic models across social media datasets. We present a set of experiments using the character n-gram Markov language model and Naive Bayes classifiers with detailed examination of what models perform best under different conditions in social media context. Experimental results show that Naive Bayes classifier based on character bi-gram model can identify the 18 different Arabic dialects with a considerable overall accuracy of 98%. This work is a first-step towards an ultimate goal of a translation system from Arabic to English and French, within the ASMAT project
{"title":"Automatic identification of arabic dialects in social media","authors":"F. Sadat, Farnaz Kazemi, Atefeh Farzindar","doi":"10.1145/2632188.2632207","DOIUrl":"https://doi.org/10.1145/2632188.2632207","url":null,"abstract":"Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for AD classification using probabilistic models across social media datasets. We present a set of experiments using the character n-gram Markov language model and Naive Bayes classifiers with detailed examination of what models perform best under different conditions in social media context. Experimental results show that Naive Bayes classifier based on character bi-gram model can identify the 18 different Arabic dialects with a considerable overall accuracy of 98%. This work is a first-step towards an ultimate goal of a translation system from Arabic to English and French, within the ASMAT project","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124200322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microblogging has recently become an integral part of the daily life of millions of people around the world. With a continuous flood of posts, microblogging services (e.g., Twitter) have to effectively handle millions of user queries that aim to search and follow recent developments of news or events. While predicting the quality of retrieved documents against search queries was extensively studied in domains such as the Web and news, the different nature of data and search task in microblogs triggers the need for re-visiting the problem in that context. In this work, we re-examined several state-of-the-art query performance predictors in the domain of microblog ad-hoc search using the two most-commonly used tweets collections with three different retrieval models that are used in microblog search. Our experiments showed that a temporal predictor was generally the best to fit the prediction task in the context of microblog search, indicating the importance of the temporal aspect in this task. The results also highlighted the need to either re-design some of the existing predictors or propose new ones to function effectively with different retrieval models that are used in our tested domain. Finally, our experiments on combining multiple predictors resulted in achieving considerable improvements in prediction quality over individual predictors, which confirmed the results reported in the literature but in different domains.
{"title":"Query performance prediction for microblog search: a preliminary study","authors":"Maram Hasanain, Rana Malhas, T. Elsayed","doi":"10.1145/2632188.2632210","DOIUrl":"https://doi.org/10.1145/2632188.2632210","url":null,"abstract":"Microblogging has recently become an integral part of the daily life of millions of people around the world. With a continuous flood of posts, microblogging services (e.g., Twitter) have to effectively handle millions of user queries that aim to search and follow recent developments of news or events. While predicting the quality of retrieved documents against search queries was extensively studied in domains such as the Web and news, the different nature of data and search task in microblogs triggers the need for re-visiting the problem in that context. In this work, we re-examined several state-of-the-art query performance predictors in the domain of microblog ad-hoc search using the two most-commonly used tweets collections with three different retrieval models that are used in microblog search. Our experiments showed that a temporal predictor was generally the best to fit the prediction task in the context of microblog search, indicating the importance of the temporal aspect in this task. The results also highlighted the need to either re-design some of the existing predictors or propose new ones to function effectively with different retrieval models that are used in our tested domain. Finally, our experiments on combining multiple predictors resulted in achieving considerable improvements in prediction quality over individual predictors, which confirmed the results reported in the literature but in different domains.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129468474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong
Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced from multiple rank learners. Base on the TREC microblog datasets, we found that our selection-based ensemble approach can significantly outperform using the single best ranker, and it also has clear advantage over the rank fusion that combines the results of all the available models.
{"title":"Ranking model selection and fusion for effective microblog search","authors":"Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong","doi":"10.1145/2632188.2632202","DOIUrl":"https://doi.org/10.1145/2632188.2632202","url":null,"abstract":"Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced from multiple rank learners. Base on the TREC microblog datasets, we found that our selection-based ensemble approach can significantly outperform using the single best ranker, and it also has clear advantage over the rank fusion that combines the results of all the available models.","PeriodicalId":178656,"journal":{"name":"Proceedings of the first international workshop on Social media retrieval and analysis","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115810904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：