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1.
Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet. 相似文献
2.
One main challenge of Named Entities Recognition (NER) for tweets is the insufficient information in a single tweet, owing to the noisy and short nature of tweets. We propose a novel system to tackle this challenge, which leverages redundancy in tweets by conducting two-stage NER for multiple similar tweets. Particularly, it first pre-labels each tweet using a sequential labeler based on the linear Conditional Random Fields (CRFs) model. Then it clusters tweets to put tweets with similar content into the same group. Finally, for each cluster it refines the labels of each tweet using an enhanced CRF model that incorporates the cluster level information, i.e., the labels of the current word and its neighboring words across all tweets in the cluster. We evaluate our method on a manually annotated dataset, and show that our method boosts the F1 of the baseline without collectively labeling from 75.4% to 82.5%. 相似文献
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4.
《Information processing & management》2019,56(6):102045
The widespread popularity and worldwide application of social networks have raised interest in the analysis of content created on the networks. One such analytical application and aspect of social networks, including Twitter, is identifying the location of various political and social events, natural disasters and so on. The present study focuses on the localization of traffic accidents. Outdated and inaccurate information in user profiles, the absence of location data in tweet texts, and the limited number of geotagged posts are among the challenges tackled by location estimation. Adopting the Dempster–Shafer Evidence Theory, the present study estimates the location of accidents using a combination of user profiles, tweet texts, and the place attachments in tweets. The results indicate improved performance regarding error distance and average error distance compared to previously developed methods. The proposed method in this study resulted in a reduced error distance of 26%. 相似文献
5.
《Information processing & management》2022,59(3):102892
Politicians’ tweets can have important political and economic implications. However, limited context makes it hard for readers to instantly and precisely understand them, especially from a causal perspective. The triggers for these tweets may have been reported in news prior to the tweets, but simply finding similar news articles would not serve the purpose, given the following reasons. First, readers may only be interested in finding the reasons and contexts (we call causal backgrounds) for a certain part of a tweet. Intuitively, such content would be politically relevant and accord with public’s recent attention, which is not usually reflected within the context. Besides, the content should be human-readable, while the noisy and informal nature of tweets hinders regular Open Information Extraction systems. Second, similarity does not capture causality and the causality between tweet contents and news contents is beyond the scopes of causality extraction tools. Meanwhile, it will be non-trivial to construct a high-quality tweet-to-intent dataset.We propose the first end-to-end framework for discovering causal backgrounds of politicians’ tweets by: 1. Designing an Open IE system considering rule-free representations for tweets; 2. Introducing sources like Wikipedia linkage and edit history to identify focal contents; 3. Finding implicit causalities between different contexts using explicit causalities learned elsewhere. We curate a comprehensive dataset of interpretations from political journalists for 533 tweets from 5 US politicians. On average, we obtain the correct answers within top-2 recommendations. We make our dataset and framework code publicly available. 相似文献
6.
《Information processing & management》2023,60(4):103325
Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. 相似文献
7.
Media sharing applications, such as Flickr and Panoramio, contain a large amount of pictures related to real life events. For this reason, the development of effective methods to retrieve these pictures is important, but still a challenging task. Recognizing this importance, and to improve the retrieval effectiveness of tag-based event retrieval systems, we propose a new method to extract a set of geographical tag features from raw geo-spatial profiles of user tags. The main idea is to use these features to select the best expansion terms in a machine learning-based query expansion approach. Specifically, we apply rigorous statistical exploratory analysis of spatial point patterns to extract the geo-spatial features. We use the features both to summarize the spatial characteristics of the spatial distribution of a single term, and to determine the similarity between the spatial profiles of two terms – i.e., term-to-term spatial similarity. To further improve our approach, we investigate the effect of combining our geo-spatial features with temporal features on choosing the expansion terms. To evaluate our method, we perform several experiments, including well-known feature analyzes. Such analyzes show how much our proposed geo-spatial features contribute to improve the overall retrieval performance. The results from our experiments demonstrate the effectiveness and viability of our method. 相似文献
8.
Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes. 相似文献
9.
《Information processing & management》2020,57(6):102312
User location data is valuable for diverse social media analytics. In this paper, we address the non-trivial task of estimating a worldwide city-level Twitter user location considering only historical tweets. We propose a purely unsupervised approach that is based on a synthetic geographic sampling of Google Trends (GT) city-level frequencies of tweet nouns and three clustering algorithms. The approach was validated empirically by using a recently collected dataset, with 3,268 worldwide city-level locations of Twitter users, obtaining competitive results when compared with a state-of-the-art Word Distribution (WD) user location estimation method. The best overall results were achieved by the GT noun DBSCAN (GTN-DB) method, which is computationally fast, and correctly predicts the ground truth locations of 15%, 23%, 39% and 58% of the users for tolerance distances of 250 km, 500 km, 1,000 km and 2,000 km. 相似文献
10.
Unstructured tweet feeds are becoming the source of real-time information for various events. However, extracting actionable information in real-time from this unstructured text data is a challenging task. Hence, researchers are employing word embedding approach to classify unstructured text data. We set our study in the contexts of the 2014 Ebola and 2016 Zika outbreaks and probed the accuracy of domain-specific word vectors for identifying crisis-related actionable tweets. Our findings suggest that relatively smaller domain-specific input corpora from the Twitter corpus are better in extracting meaningful semantic relationship than generic pre-trained Word2Vec (contrived from Google News) or GloVe (of Stanford NLP group). However, domain-specific quality tweet corpora during the early stages of outbreaks are normally scant, and identifying actionable tweets during early stages is crucial to stemming the proliferation of an outbreak. To overcome this challenge, we consider scholarly abstracts, related to Ebola and Zika virus, from PubMed and probe the efficiency of cross-domain resource utilization for word vector generation. Our findings demonstrate that the relevance of PubMed abstracts for the training purpose when Twitter data (as input corpus) would be scant during the early stages of the outbreak. Thus, this approach can be implemented to handle future outbreaks in real time. We also explore the accuracy of our word vectors for various model architectures and hyper-parameter settings. We observe that Skip-gram accuracies are better than CBOW, and higher dimensions yield better accuracy. 相似文献
11.
《Information processing & management》2022,59(2):102820
As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights.UsaGeoCov19 link: http://yunhefeng.me/geo-tagged_twitter_datasets/. 相似文献
12.
《Information processing & management》2019,56(3):1119-1132
Recently, geolocalisation of tweets has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geotagged tweets available to enable such tasks remains insufficient. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data and has consequences for a wide range of applications. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets, where each vote is weighted based on evidence gathered from the ranking. Using geotagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms state-of-the-art baselines in terms of accuracy and error distance, in both cities, with the cost of decreased coverage. Finally, we investigated the applicability of our method in a real-time scenario by means of a traffic incident detection task. Our analysis shows that our fine-grained geolocalisation method can overcome the limitations of geotagged tweets and precisely map incident-related tweets at the real location of the incident. 相似文献
13.
《Information processing & management》2016,52(5):873-884
OCR errors in text harm information retrieval performance. Much research has been reported on modelling and correction of Optical Character Recognition (OCR) errors. Most of the prior work employ language dependent resources or training texts in studying the nature of errors. However, not much research has been reported that focuses on improving retrieval performance from erroneous text in the absence of training data. We propose a novel approach for detecting OCR errors and improving retrieval performance from the erroneous corpus in a situation where training samples are not available to model errors. In this paper we propose a method that automatically identifies erroneous term variants in the noisy corpus, which are used for query expansion, in the absence of clean text. We employ an effective combination of contextual information and string matching techniques. Our proposed approach automatically identifies the erroneous variants of query terms and consequently leads to improvement in retrieval performance through query expansion. Our proposed approach does not use any training data or any language specific resources like thesaurus for identification of error variants. It also does not expend any knowledge about the language except that the word delimiter is blank space. We have tested our approach on erroneous Bangla (Bengali in English) and Hindi FIRE collections, and also on TREC Legal IIT CDIP and TREC 5 Confusion track English corpora. Our proposed approach has achieved statistically significant improvements over the state-of-the-art baselines on most of the datasets. 相似文献
14.
Rich Ling Johannes Bjelland Pål Roe Sundsøy Scott W. Campbell 《The Information Society》2014,30(4):282-291
This article examines how we use mobile telephony to maintain our physically and socially closest social circle. The analysis is based on traffic data gathered from Norway using approximately 24 million calls and texts made by private individuals. Previous research has shown that our temporal and spatial movement is highly predictable and that the majority of calls and text messages are sent to only four to six different persons. This article extends this research by examining both tie strength and the distance between the interlocutors in urban and rural settings. The findings show that even as information and communication technologies (ICTs) potentially put the world at our fingertips, the mobile phone is an instrument of a more limited geographical and social sphere. Approximately two-thirds of our calls/texts go to strong ties that are within a 25-km radius. 相似文献
15.
Journalists, emergency responders, and the general public use Twitter during disasters as an effective means to disseminate emergency information. However, there is a growing concern about the credibility of disaster tweets. This concern negatively influences Twitter users’ decisions about whether to retweet information, which can delay the dissemination of accurate—and sometimes essential—communications during a crisis. Although verifying information credibility is often a time-consuming task requiring considerable cognitive effort, researchers have yet to explore how people manage this task while using Twitter during disaster situations.To address this, we adopt the Heuristic-Systematic Model of information processing to understand how Twitter users make retweet decisions by categorizing tweet content as systematically processed information and a Twitter user’s profile as heuristically processed information. We then empirically examine tweet content and Twitter user profiles, as well as how they interact to verify the credibility of tweets collected during two disaster events: the 2011 Queensland floods, and the 2013 Colorado floods. Our empirical results suggest that using a Twitter profile as source-credibility information makes it easier for Twitter users to assess the credibility of disaster tweets. Our study also reveals that the Twitter user profile is a reliable source of credibility information and enhances our understanding of timely communication on Twitter during disasters. 相似文献
16.
《Information processing & management》2020,57(5):102219
GPS-enabled devices and social media popularity have created an unprecedented opportunity for researchers to collect, explore, and analyze text data with fine-grained spatial and temporal metadata. In this sense, text, time and space are different domains with their own representation scales and methods. This poses a challenge on how to detect relevant patterns that may only arise from the combination of text with spatio-temporal elements. In particular, spatio-temporal textual data representation has relied on feature embedding techniques. This can limit a model’s expressiveness for representing certain patterns extracted from the sequence structure of textual data. To deal with the aforementioned problems, we propose an Acceptor recurrent neural network model that jointly models spatio-temporal textual data. Our goal is to focus on representing the mutual influence and relationships that can exist between written language and the time-and-place where it was produced. We represent space, time, and text as tuples, and use pairs of elements to predict a third one. This results in three predictive tasks that are trained simultaneously. We conduct experiments on two social media datasets and on a crime dataset; we use Mean Reciprocal Rank as evaluation metric. Our experiments show that our model outperforms state-of-the-art methods ranging from a 5.5% to a 24.7% improvement for location and time prediction. 相似文献
17.
《Information processing & management》2020,57(2):102145
Social networks like Twitter are good means for people to express themselves and ask for help in times of crisis. However, to provide help, authorities need to identify informative posts on the network from the vast amount of non-informative ones to better know what is actually happening. Traditional methods for identifying informative posts put emphasis on the presence or absence of certain words which has limitations for classifying these posts. In contrast, in this paper, we propose to consider the (overall) distribution of words in the post. To do this, based on the distributional hypothesis in linguistics, we assume that each tweet is a distribution from which we have drawn a sample of words. Building on recent developments in learning methods, namely learning on distributions, we propose an approach which identifies informative tweets by using distributional assumption. Extensive experiments have been performed on Twitter data from more than 20 crisis incidents of nearly all types of incidents. These experiments show the superiority of the proposed approach in a number of real crisis incidents. This implies that better modelling of the content of a tweet based on recent advances in estimating distributions and using domain-specific knowledge for various types of crisis incidents such as floods or earthquakes, may help to achieve higher accuracy in the task. 相似文献
18.
《Information processing & management》2020,57(6):102309
Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge. 相似文献
19.
Coronavirus related discussions have spiraled at an exponential rate since its initial outbreak. By the end of May, more than 6 million people were diagnosed with this infection. Twitter witnessed an outpouring of anxious tweets through messages associated with the spread of the virus. Government and health officials replied to the troubling tweets, reassuring the public with regular alerts on the virus's progress and information to defend against the virus. We observe that social media users are worried about Covid 19-related crisis and we identify three separate conversations on virus contagion, prevention, and the economy. We analyze the tone of officials’ tweet text as alarming and reassuring and capture the response of Twitter users to official communications. Such studies can provide insights to health officials and government agencies for crisis management, specifically regarding communicating emergency information to the public via social media for establishing reassurance. 相似文献