共查询到20条相似文献，搜索用时 15 毫秒
1.
《Information processing & management》2023,60(1):103168
Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner. 相似文献
2.
《Research Policy》2022,51(7):104558
Clean energy technologies are important for meeting long-term climate and competitiveness goals. But clean energy industries are part of global value chains (GVCs), where past manufacturing shifts from developed to emerging economies have raised questions on a decline in long-term innovation. Our research centers on how geographic shifts in the GVC shape long-term innovation, i.e., innovation in a time frame within which “mission-oriented”, societal, or firm strategic objectives need to be met rather than tactical, near-term market competitiveness alone. Focusing on wind energy, we introduce a temporal measure to distinguish between long-term and short-term innovation, applying natural language processing methods on patent text data. We consider supply-side value chain factors (i.e., manufacturing supplier relationships with original equipment manufacturers (OEMs)) and demand-side factors (i.e., policy-induced clean energy market growth), shaping the patenting activities of 358 global specialized wind suppliers (2006–2016). Our findings suggest that the wind industry did not suppress long-term innovation during manufacturing shifts, in this case to China. After 2012 when China developed a large wind market, long-term innovation increased by 80.7% in European suppliers working with non-European OEMs (including Chinese) and by 67.2% in Chinese suppliers working with non-Chinese OEMs. Our results highlight the importance of coupling international manufacturing relationships with sizeable local demand for inducing long-term innovation. Our results advance research in innovation, GVCs, and green industrial policy with implications for several industries that can contribute to climate mitigation. 相似文献
3.
The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing. 相似文献
4.
《Information processing & management》2020,57(6):102303
Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created. 相似文献
5.
6.
Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy. 相似文献
7.
《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
8.
Tingting Yang Fei Li Donghong Ji Xiaohui Liang Tian Xie Shuwan Tian Bobo Li Peitong Liang 《Information processing & management》2021,58(6):102681
Depression is a widespread and intractable problem in modern society, which may lead to suicide ideation and behavior. Analyzing depression or suicide based on the posts of social media such as Twitter or Reddit has achieved great progress in recent years. However, most work focuses on English social media and depression prediction is typically formalized as being present or absent. In this paper, we construct a human-annotated dataset for depression analysis via Chinese microblog reviews which includes 6,100 manually-annotated posts. Our dataset includes two fine-grained tasks, namely depression degree prediction and depression cause prediction. The object of the former task is to classify a Microblog post into one of 5 categories based on the depression degree, while the object of the latter one is selecting one or multiple reasons that cause the depression from 7 predefined categories. To set up a benchmark, we design a neural model for joint depression degree and cause prediction, and compare it with several widely-used neural models such as TextCNN, BiLSTM and BERT. Our model outperforms the baselines and achieves at most 65+% F1 for depression degree prediction, 70+% F1 and 90+% AUC for depression cause prediction, which shows that neural models achieve promising results, but there is still room for improvement. Our work can extend the area of social-media-based depression analyses, and our annotated data and code can also facilitate related research. 相似文献
9.
《Information processing & management》2022,59(2):102807
Social sensing has become an emerging and pervasive sensing paradigm to collect timely observations of the physical world from human sensors. In this paper, we study the problem of geolocating abnormal traffic events using social sensing. Our goal is to infer the location (i.e., geographical coordinates) of the abnormal traffic events by exploring the location entities from the content of social media posts. Two critical challenges exist in solving our problem: (i) how to accurately identify the location entities related to the abnormal traffic event from the content of social media posts? (ii) How to accurately estimate the geographic coordinates of the abnormal traffic event from the set of identified location entities? To address the above challenges, we develop a Social sensing based Abnormal Traffic Geolocalization (SAT-Geo) framework to accurately estimate the geographic coordinates of abnormal traffic events by exploring the syntax-based patterns in the content of social media posts and the geographic information associated with the location entities from the social media posts. We evaluate the SAT-Geo framework on three real-world Twitter datasets collected from New York City, Los Angeles, and London. Evaluation results demonstrate that SAT-Geo significantly outperforms state-of-the-art baselines by effectively identifying location entities related to the abnormal traffic events and accurately estimating the geographic coordinates of the events. 相似文献
10.
Health misinformation has become an unfortunate truism of social media platforms, where lies could spread faster than truth. Despite considerable work devoted to suppressing fake news, health misinformation, including low-quality health news, persists and even increases in recent years. One promising approach to fighting bad information is studying the temporal and sentiment effects of health news stories and how they are discussed and disseminated on social media platforms like Twitter. As part of the effort of searching for innovative ways to fight health misinformation, this study analyzes a dataset of more than 1600 objectively and independently reviewed health news stories published over a 10-year span and nearly 50,000 Twitter posts responding to them. Specifically, it examines the source credibility of health news circulated on Twitter and the temporal, sentiment features of the tweets containing or responding to the health news reports. The results show that health news stories that are rated low by experts are discussed more, persist longer, and produce stronger sentiments than highly rated ones in the tweetosphere. However, the highly rated stories retained a fresh interest in the form of new tweets for a longer period. An in-depth understanding of the characteristics of health news distribution and discussion is the first step toward mitigating the surge of health misinformation. The findings provide insights into understanding the mechanism of health information dissemination on social media and practical implications to fight and mitigate health misinformation on digital media platforms. 相似文献
11.
The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments. 相似文献
12.
In-Su Kang Pyung Kim Seungwoo Lee Hanmin Jung Beom-Jong You 《Information processing & management》2011
Author disambiguation resolves same-name author occurrences in the bibliographic data into namesakes. This enables author-centered searches and high-quality social network analysis. As an attempt to promote much research in author disambiguation, KISTI have constructed a new large-scale test set for this field. This article describes its semi-manual creation procedures, characteristics especially in terms of author ambiguities and name diversities. In addition, the baseline performance of author clustering against the test set is provided. 相似文献
13.
Social networks are becoming a key communication tool for organizations, but also for top managers like CEOs. Among the different available platforms, Twitter is one of the greatest and it is considered one of the most suitable to share information and engage in dialogue with stakeholders. In this way, this paper analyzes the presence of CEOs on the most active social network sites, and assess the activity and interaction of these top managers on Twitter. CEOs from Global and Latin American companies were selected, to compare their performance. The results of the study show that the presence of CEOs in social networks is very low, and the majority of those that are present on them are not adequately using their Twitter accounts. Although the general presence and performance on are low, LatAm CEOs have a better presence on social networks and they are more active on Twitter, but Global CEOs have better interaction results on their accounts. So, this area of strategic communication should be improved by communication practitioners, since the CEO communication is nowadays a key communication issue for any organization. 相似文献
14.
Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
15.
16.
This study examines the extent to which politicians' visibility in traditional news coverage explains individual politicians' visibility on social media, and vice versa. We also explore whether these relationships depend on commonly identified characteristics of individual politicians. We collected data for all elected candidates from the 2012 Dutch national elections covering each 15 days prior to the election day (N = 2250). This includes 2736 newspaper articles and 77,597 mentions on Facebook and Twitter. Our results show that the traditional news agenda and social media agenda impact each other, but that the reciprocal influence is not independent of politician characteristics. 相似文献
17.
《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
18.
Despite various models established to present the process of information encountering (IE), little research has been done on the stimulus that plays an essential role in attracting users’ attention and eliciting the subsequent behavioral responses during an IE process. This study was particularly interested in visual stimuli which are superior to textual ones in enhancing information processing and sensory experience. A diary study of IE was conducted in the context of micro-blogging services. They demonstrate the environmental characteristics conducive to IE and are especially abundant in visual stimuli. A total of 189 valid IE incidents triggered by visual stimuli on a representative micro-blogging service were collected with an online questionnaire created based on the critical incident technique (CIT) and analyzed both quantitatively and qualitatively. As found in this study, most of the visual stimuli triggering IE excluded text or motion, and their comprehensibility and novelty were both perceived to be high while humorousness much lower. The encountered micro-posts covered a wide range of topics and were published by different types of micro-bloggers. When interacting with the posts, the participants sometimes just examined their visual or textual content, but sometimes further captured the posts by liking, reposting, commenting, and following, etc. The significant results indicate that the visual stimuli excluding text and those with higher comprehensibility or humorousness were more likely to induce intense approach to the micro-posts. These findings inform micro-bloggers of the means of engaging the audience in intense interactions with their posts to gain persistent profit or reputation. The combination of diaries and the CIT is effective for data collection in IE research. 相似文献
19.
There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare. 相似文献
20.
《Information processing & management》2020,57(6):102313
Social media is widely used for sharing disaster-related information following natural disasters. Drawing on negativity bias theory, integrated crisis mapping model, and arousal theory, this study characterized the emotional responses of the public and tested the way emotional factors and influential users (with high numbers of followers and activeness) affect the number of reposts. Results indicated that after unpredictable earthquakes, the public showed negative responses, and negativity bias theory manifested especially when the posts came from influential users. During a typhoon or earthquake, the number of reposts grew as the number of anger-related words in posts increased. Anxiety- and typhoon-related posts from users with high numbers of followers negatively affected the number of reposts, whereas sadness-related posts had contrasting effects. These findings can help emergency managers formulate proper emotional response strategies after various natural calamities and help researchers test the abovementioned theories or models using real-word data from social media. 相似文献