dataesr/scientific-paragraphs-categorization huggingface dataset:
GeoNLPlify specialization and spatial_equivalence obtain the smallest evaluation loss. By exposing the models to new and multiple combinations of label and location during the training, GeoNLPlify helps models to better generalize when they encounter new combinations during the evaluation. Indeed, the overfitting reduced by GeoNLPlify seems to come from a too strong association between a place and the types of events that happened there in the training. Finally, and contrary to our empirical intuition, the level of the spatial hierarchy does not matter. Indeed, in Section 3, we had shown that spatial levels were associated with certain labels as illustrated by Fig. 4  in the training non-augmented dataset. For example, newspapers reporting on a flood will use city-level locations, while those talking about the consequences of a crisis will report at the country level.
Potential source of bias One of the potential risks of using data augmentation is the introduction of new biases into the data. This is particularly true for textual data, where the use of natural language can introduce subtle biases that are difficult to detect. The risk that we identify when using GeoNLPlify is that real links between labels and geographical entities may be unlearned by the models. To estimate this, we propose to analyze the texts for which the models trained with GeoNPlify make a classification error while the models trained without augmentation do not. The Table  7  shows the results for PADI-web data of the first fold for each GeoNPLify augmentation and combination. Thus for each augmentation, we apply two models (augmentation and without augmentation) on 10% of the PADI-web dataset, i.
Exploring other dimensions For crisis-related corpora such as PADI-web or CrisisNLP, spatial information is ubiquitous and can lead to overfitting between class and spatial entities. It is conceivable that this type of overfitting may occur in other dimensions of information as well. For instance, the temporal dimension could also be explored. As demonstrated in this study by using model explicability methods like LIME  [10]  or SHAP  [32] , the models might unfortunately learn to classify certain documents based on the temporal entities found in the text. For example, in the context of crisis management, if the training dataset contains a large number of documents about floods that occurred in 2018, would the classification model tend to classify all new documents containing "2018" as flood events? If so, data augmentation by varying this dimension could mitigate this overfitting bias and facilitate domain transfer, such as training a model on a flood corpus to use it on a wildfire event for example.
Conclusion In conclusion, this paper presents a significant contribution to the field of text classification by demonstrating the potential of exploiting spatial information to improve text classification performance on crisis-related data. Our study demonstrates the issue of BERT-like models overfitting on words carrying spatial information, and offers a solution, GeoNLPlify, a set of data augmentation methods that reduces significantly the bias of spatial overfitting. Our experiments show that all NLP data augmentation used in this paper reduce overfitting but GeoNLPlify goes one step further and outperforms existing data augmentation techniques on multiple datasets, indicating its effectiveness in enhancing the performance of language models on text classification tasks. However, this study also raises important questions about the representation of spatial information by language models.
3. We investigated the influence of crop phenology on the behaviour of European roe deer (Capreolus capreolus) to infer the functional roles of crop types in the food-cover landscape. We analysed the habitat use and activity patterns of 105 GPS-collared female roe deer using a unique dataset combining field-specific land cover data, region-specific estimates of crop phenology and weekly harvesting data for three common crops in a French agroecosystem. 4. We found very distinct habitat use and activity patterns according to crop type, phenological stage and time of day. Wheat and artificial meadows were strongly selected at night-time during the early and post-harvest stages only, when roe deer were highly active, suggestive of feeding activity. On the contrary, roe deer strongly selected maize during the day when it was high enough to provide cover, when they were less active, indicating that it was primarily used for refuge.
To do so, we used a unique dataset combining detailed annual land cover data, weekly estimates of crop phenology for three crop types and tracking data from 105 GPS-monitored animals living in an agroecosystem. This enabled us to map the availability of cover across the landscape at a fine spatio-temporal scale and disentangle the influence of plant phenology and harvesting events on crop use. The roe deer is a particularly appropriate species for this investigation as it thrives in agricultural landscapes  (Hewison et al. , 2009)  due to its high ecological and behavioural plasticity  (Andersen et al. , 1998). Little is known, however, about how roe deer navigate spatio-temporal variations in the availability of food and cover that are shaped by human practices in agroecosystems, nor how changes in these practices might modify the species' food-safety trade-off.
We contrasted patterns of use during night and day for each crop type and phenological stage by performing post-hoc contrast analyses on our previous model predictions for crop use and adjusting for multiple comparisons using the Dunnett adjustment method  (Dunnett, 1964). We classified activity sensor data into active versus inactive behaviour by using the thresholds of activity level specified by  Bonnot et al. (2020)  who worked on the same dataset. Since we considered phenological stages that were common to all crop types, we fitted a single binomial GLMM with a logit link function to analyse variation in activity in relation to crop type, phenological stage (emergence/maturity/post-harvest) and time of day (night/day), including a random intercept for individual identity to control for repeated observations.
Discussion There is a growing need to better understand how human-driven resource dynamics influence wildlife behaviour in shared areas to promote sustainable coexistence  (Abrahms et al. , 2021). In this context, our study provides insight on how synanthropic species that are successful in heavily human-impacted landscapes can cope with rapid and sudden modifications of their environment (e. g. , cultivation) through behavioural plasticity. We found strong evidence that roe deer adjust their behaviour to spatio-temporal variations in crop phenology within a heterogeneous agroecosystem. Past work on fine-scale responses to crop phenology, mainly on wild ungulates  (Branco et al. , 2019; Paolini et al. , 2018) , focused solely on the role of crops as food subsidies. Our unique dataset enabled us to evaluate the potential role of crops as alternative cover habitat for roe deer and to explore how crop phenology and landscape composition influence the trade-off between resource acquisition and risk avoidance, especially when access to 'natural' cover is limited.
Acknowledgements We thank the local hunting associations, the  Fédération Départementale des Chasseurs de la Haute Garonne , and numerous co-workers and volunteers for their assistance. We thank the  Observatoire Spatial Régional (CNRS/INSU)  as well as the  Agir research group (INRAE)  for providing local weather station data. We thank the  Regional Chambre of Agriculture Occitanie  and the  Departmental Chambre.
Acknowledgements ML and JFA were supported by the " Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). NG received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie grant  agreement  BIOFOODWEB  (No  101025471 ). JFA thanks  Mathew Leibold  and members of the  Leibold Lab  for their encouraging and thoughtful comments during the writing of this manuscript.
We first test our contingency framework with the help of a unique data set containing more than 97,000 Amazon reviews from 46 products to investigate how review-, reviewer-, and product-related characteristics affect the impact of images on review helpfulness. Subsequently, we analyze more than 6,000 review images posted within our sample and investigate how image focus and image context fit -in interaction with review-, reviewer-, and product-related factors -affect review helpfulness. In doing so, we make three important contributions to the stream of online review research: First, we investigate in a real e-commerce setting how the impact of review images on review helpfulness is contingent on review valence, review length, and reviewer reputation. Second, we investigate, how the impact of review images differs for search vs.
Data We test our conceptual model with the help of a unique data set of online reviews from Amazon. Amazon incorporates more than 250 million reviews from over 213 million customers for 353 million products ( LandingCube 2022 ), thus  X Digital Photo Frame X X Smartphone X X Computer Adapter X X Printer X X Electric Toothbrush X X Mascara X X Lipstick X X Party Dress X X Microwave Oven X X Vacuum Cleaner X X Razor X X providing enough representative reviews and review helpfulness votes for a variety of products and brands. We obtain all reviews in our sample with the help of a self-coded R-based web crawler that extracts relevant review content and images via the rvest package  ( Wickham 2016 )  as described in  Yildirim and Kübler (2023 , pp. 147-156). To identify suitable reviews to test the different contingencies of our conceptual model, we conduct multiple pretests.
Data collection and variable operationalization For each product, we obtain all reviews posted on Amazon before April 2019, which results in 97,997 reviews in total. For each review, we collect the review text, star rating, reviewer badges, number of helpful votes, and all images included in the review. We also collect the review date and include the age of the review into our model to control for Amazon's ranking algorithm which displays reviews that have received more helpful votes more prominently than other reviews. Hence, newer reviews stay farther down the page and have less chance of receiving votes  ( Salehan and Kim 2016 ). Further, a visual inspection of the variable distributions as well as the corresponding box plots indicates that both review helpfulness and the number of images face outlier problems.
Consequently, it is essential to better understand how image content affects review helpfulness. For this purpose, we conduct a follow-up study based on the subset of reviews from our data set that contain images. The filtered sample contains 6,060 images from 3,784 reviews. Next to the variables already included in our main study, we additionally create two different variables capturing image content, i. e. , image focus and context fit. First, image focus indicates whether the image shows the product itself (e. g. , pack shot) or its application. When creating a review, consumers can decide whether to add an image showing only the product or the product in use. We assume -given that most online stores already provide consumers with detailed product images -that those images provide little new information compared to images showing the product in application and thus do not help (much) to overcome information asymmetry.
Acknowledgments We extend our sincere gratitude to the Editors in Chief,  Katrijn Gielens  and  Anne Roggeveen , for their invaluable and most insightful guidance during the development of this manuscript. Additionally, we express our appreciation to the anonymous Associate Editor and the three reviewers for their constructive and enriching comments, significantly contributing to the advancement of this work. Furthermore, we acknowledge and appreciate our numerous colleagues who generously shared their feedback during various conferences and workshops, enhancing the quality of this manuscript.
In recent times, news medias avail oneself of online social media platforms for news promotion, sharing and commentary to a large extent mainly in Twitter, Facebook, and Reddit. Therefore, in the literature, researchers have been used machine learning and text mining techniques to attain useful insights from the news media data in social media in-order to understand the factors for gaining large audience attention. Different to the previous studies, analyses of the news media in this work are based on a set of new features; content features such as the originality of a news item, context features such as time and circadian patterns of a news media, and reader reactions. Our dataset includes 238K tweets and 128K Facebook posts of 48 most popular news medias shared during May-June 2017. In this study we explored; news producers, news consumers, inter news production patterns, inter news dissemination behaviors, sharing similar news items within Twitter and Facebook (cross-posts), and news readers reactions on news items.
In addition, we identify news media popularity parameters and circadian behaviors of popular news media in order to explore their publishing patterns. Finally, we propose a reader reaction predictive model for news media to increase news popularity based on the considered features in this work. Moreover, unlike the literature we considered news medias that are popular worldwide rather than choosing from a specific geographic location. To that end, we used data mining, text mining and statistical methods to perform descriptive and predictive analyses on the dataset crawled from 48 news medias presence in Facebook and Twitter. The descriptive analyses mainly use the result from ConOrigina framework to explore news origination patterns, news dissemination patters and news popularity parameters coalescing both content features and contextual features.
Then, predictive analyses examine how contextual features and historical behaviors (number of published posts, number of followers) can combine to predict reader engagement in future. In section II, we provide state of the art technologies and methods used in the news media analysis. Section III gives the detailed information about the dataset we used in this work. Section IV shows the descriptive statistics of the dataset and details on the predictive analysis is presented in IV-D3. Finally, in section V, we conclude the paper with the results identified in the previous sections. Traditional news providers are loosing their traditional income streams due to arrival of social netwroking services allowing any user in the network to share and propagate news articles freely and easily. Therefore, in order to survive those news media, they need to understand the dynamics of news production, dissemination and consumption in social media.
In particular, Twitter has been identified as a source of breaking news  [12]  [13] and these breaking news get attraction from the readers before other social media sites like Facebook and Google Plus  [18]. Hence, in the following sections we survey how news papers and news media use Facebook and Twitter to disseminate news and how can those news media can increase the news readers' attention. The news posts are different to the other types of the posts in social media those that target on personal details, events and jokes. Hence, previous research studies target on which attribute need to be considered to get the attention from the news readers using the real datasets from social media. News posts typically contain links to the news article or small reports on a event. These news posts has three different features including content features (what is written), user features (details on the who is posted) and context features (time, location).
A. DATA COLLECTION The analysis of this work is based on a dataset collected from top 48 most popular news media (English edition) ranked by Alexa 1  retrieved on 2 nd May 2017. Firstly, a set of active and authorized user-accounts/user-IDs (135 user-IDs) were manually identified from Facebook and Twitter. Subsequently, for each user-ID, Facebook and Twitter crawlers were executed to extract time-line posts and their respective timestamps within one month period starting from 8 th May to 8 th June 2017. We believe that the posts published during one-month is sufficient to analyze the propagation patterns of news content as news medias tend to share news items frequently in the social media. Since Twitter RESTful API allows only to retrieve last 3200 posts, data collection is started on 8 th May 2017 and the crawler is executed every 5 days to retrieve a full dataset of one month from 8 th May to 8 th June 2017.
B. DATASET DESCRIPTION Table  1  shows a brief description about the dataset acquired from Facebook and Twitter. A handful of news media has multiple user-IDs to represent different categories (technology, politics, breaking news, etc. ), and others use only one legal user-ID. Therefore, we consider two different scenarios to explain the dataset; multi-user-ID scenarios (exhibits multiple user accounts per news media) and single-user-ID scenarios (exhibits a single user account per news media). In Multi-user-ID scenarios, we look attentively at the full distribution of the dataset belongs to the news medias considering total of 270 users selected from the Facebook (135 users) and Twitter (135 users). In Single-user-ID scenarios, dataset consists of 96 users selected from the Facebook (48 users) and Twitter (48 users) and shows its full distribution in which, single user is chosen based on the highest #followers compared with the other users identified from the same news media.
Following that, Table  1  includes a brief description of the dataset for both Multi-user-ID and single user-ID scenarios separately for Twitter and Facebook. It gives the information about average #followers in each scenario and total #posts shared. To better explain the distribution of #posts shared in Twitter and Facebook for two scenarios, we generated violin plots shown in Figure  1. These plots illustrate the abstract representation of the probability distribution of the dataset based on the symmetrical kernel density estimation (KDE) showing the variability of data.
2) Single-user-ID scenarios As mentioned in the previous sections, Single-user-ID scenarios consider only one user-ID per news media, the most popular Twitter and Facebook user of the news media with highest #followers compared to other users who represent the same news group. In total, we selected the content of 96 user-IDs (48 each from Facebook and Twitter) with additional information such as #posts shared and #followers as detailed in Table  2. The violin plot shown in Figure  1b  shows the distribution of pre-processed dataset from singleuser-ID scenarios, indicating that less #posts have shared on Facebook than on Twitter. In the Facebook dataset, #posts ranges from 0 to 3. 7K (median: 1476) and that of Twitter is from 0 to 9K (median: 2629). The median value is more indicative of the fact that majority of the content is posted by the news media in Twitter.
Furthermore, the medians of both distributions reside in the higher probability range of the KDE. However, Twitter dataset is skewed to the right while Facebook dataset is skewed to the left. Apart from that, Figure  1b  indicates an outliers, a single Facebook user-ID, as having the highest #posts in the entire dataset. Analysis of this work is based on the Single-user-ID scenarios. Table  2  shows the considered user-IDs for each news media, #posts shared by them and its #followers. In order to visualize the distribution of posts in the Single-user-ID scenarios, we constructed a bar graph shown in Figure  2. A closer look at the distribution of this data indicates that the highest #posts (10. 6K) were shared by the Facebook user-ID of Indianexpress, followed by Hindustantimes(6. 7K). In contrast, five news media in Twitter were posted more than 85K posts during the crawling period: Thehill, Bloomberg, Hindustantimes, Theguardian, and Indianexpress.
A. FRAMEWORK DESCRIPTION The ConOrigina framework was designed to identify textual content originator in OSNs based on the SCAP method to detect linguistic patterns and online circadian behavior to detect temporal patterns of the user  [5]. The ConOrigina uses four distinct phases; i) Data-cralwer, ii) Pre-processor, iii)  Feature-extractor, and iv) Author-analyzer. Data-crawler extracts relevant information from OSNs to build a knowledge base, mainly author details, and content attributes. The use of raw data in ConOrigina reduces the accuracy of results as we crawled data from different social medias that use various types of content properties (e. g, length) and user profiles. Pre-processor in ConOrigina consider two criteria to filter raw dataset; 1) remove posts with word-count=0 and word-count<3.
Many Facebook posts contain images without any caption. This step helps to eliminate the posts without any textual content. Additionally, some tweets may have only 1-2 words as tweets are short messages of at most 140 characters. By removing very short-length tweets we can improve the efficiency of our system with the n-gram detection models. 2) remove URLs. As many tweets and Facebook posts are embedded with URLs, the removal of URLs from texts increase the efficiency when identifying replicas. The main attributes used with ConOrigina are textual content of a news item and its timestamp and hence, removing URLs from the text will not be affected when detecting the originator. Feature-extractor receives as input a dataset classified in the pre-processor phase and outputs an average similarity index per news media based on their similar writing patterns.
1) News originators in Facebook In Figure  3a  SDa state transitions of news media in Facebook are indicated under the 'Facebook' label and therefore label 2a represents the portion of content originated by its Facebook user itself. As shown in Figure  3a , almost all news medias (about 96%) accounts in Facebook have shared a higher portion of content originated by themselves than replicas. An interesting observation is that, in our dataset, Reddit and Cbsnews have shared less self-originated content and more replicas, and it is more visible in Figure  3a. Another observation is that, 49% of the Huffingtonpost news items are non-classified, 36% of the posts are self-originated in Facebook, and 18% of replicas that were first published by its own Twitter user account. The main reason for classifying many Facebook posts belongs to Huffingtonpost as nonclassified is that the lengths of many posts published by Huffingtonpost are only 4-6 words, which is smaller when compared with the posts of other users.
In Figure  4 , the diagonal represents news media those who exhibits cross-posts between their own Facebook and Twitter user account. A small number of news media namely, DW, Wunderground and Alternet, do not exhibit any cross-posts between their own Facebook and Twitter users. Cnn disclosed the largest portion of the cross-posting behavior (1K posts) in which, approximately 13% of its Facebook posts were exactly similar to its tweets. Besides that, about 70% of the cross-posts of Cnn in Twitter was shared with the Facebook user of the Cbsnews, which is equal to the 13. 8% of the total posts shared by Cbsnews in Facebook. Another interesting observation from Figure  4  is that, Time in Twitter has shown cross-posts among 40 different news medias in Facebook. The second-largest number of cross-posts in our dataset was belongs to Time in which, majority of them were shared within its own Facebook user (about 15.