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Evaluation of researchers' output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as the h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers' scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers and their organizations seeking solutions to improve it. This study investigates the effect of the author, paper/venue-specific features on the future h-index. For this purpose, we used a machine learning approach to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it 'prior impact-based features' and includes the number of publications, received citations, and h-index. The second group is 'non-prior impact-based features' and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting researchers' h-index in three career phases. Also, we examined the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author's characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. Although the results demonstrate better performance for the models containing prior impact-based features for all researchers' groups in the near future, we found that non-prior impact-based features are more robust predictors for younger scholars in the long term. Also, prior impact-based features lose their power to predict more than other features in the long term.

Author affiliations provide key information when attributing academic performance like publication counts. So far, such measures have been aggregated either manually or only to top-level institutions, such as universities. Supervised affiliation resolution requires a large number of annotated alignments between affiliation strings and known institutions, which are not readily available. We introduce the task of unsupervised hierarchical affiliation resolution, which assigns affiliations to institutions on all hierarchy levels (e.g. departments), discovering the institutions as well as their hierarchical ordering on the fly. From the corresponding requirements, we derive a simple conceptual framework based on the subset partial order that can be extended to account for the discrepancies evident in realistic affiliations from the Web of Science. We implement initial baselines and provide datasets and evaluation metrics for experimentation. Results show that mapping affiliations to known institutions and discovering lower-level institutions works well with simple baselines, whereas unsupervised top-level- and hierarchical resolution is more challenging. Our work provides structured guidance for further in-depth studies and improved methodology by identifying and discussing a number of observed difficulties and important challenges that future work needs to address.

AbstractThe governments' mitigation measures to fight the COVID-19 pandemic are unprecedented in our post-war history. For overcoming this crisis, citizens were expected to act in compliance with these measures in order to control the spread of the virus and keep public health systems functional. This call for protecting the public health at the same time confronted citizens with several and severe limitations of their democratic freedoms and rights: confinement, restriction on freedoms of movement, religion, specific provisions for public protest and finally also limitations to the right of education by school closures. This paper analyzes how citizens perceive the threat the COVID-19 pandemic and especially the mitigation measures posed for democracy. We assume that pandemic waves and pandemic fatigue have an impact on the perception of threat. To see the overall societal picture, we exploit a large-scale archive of online discourse on Twitter out of which we extract democracy-related discourse with the same temporal and geospatial coverage for our investigation. From that data source, we apply computational methods to extract time series data reflecting aggregated opinions and their evolution over time concerned with the correlation of attitudes towards democracy. We them move deeper using a longitudinal panel survey we conducted in November/December 2020, March/April 2021, and July/August 2021. to have a view of the relationship between citizens' socio-economic status and basic political attitudes. Our multi-method analysis bases on the German case and covers the period from December 2020 to August 2021.

Open Access (OA) facilitates access to research articles. However, authors or funders often must pay the publishing costs, preventing authors who do not receive financial support from participating in OA publishing and gaining citation advantage for OA articles. OA may exacerbate existing inequalities in the publication system rather than overcome them. To investigate this, we studied 522,411 articles published by Springer Nature. Employing correlation and regression analyses, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing model, and the citation impact of their papers. A machine learning classification method helped us to explore the importance of different features in predicting the publishing model. The results show that authors eligible for article processing charge (APC) waivers publish more in gold OA journals than others. In contrast, authors eligible for an APC discount have the lowest ratio of OA publications, leading to the assumption that this discount insufficiently motivates authors to publish in gold OA journals. We found a strong correlation between the journal rank and the publishing model in gold OA journals, whereas the OA option is mostly avoided in hybrid journals. Also, results show that the countries' income level, seniority, and experience with OA publications are the most predictive factors for OA publishing in hybrid journals.

Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user's knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user's knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.

The web and the social web play an increasingly important role as an information source for Members of Parliament and their assistants, journalists, political analysts and researchers. It provides important and crucial background information, like reactions to political events and comments made by the general public. The case study presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets an effective exploration of political web archives. In this paper, we describe semantic technologies deployed to ease the exploration of the archived web and social web content and present evaluation results. ; EC/ARCOMEM ; ERC/ALEXANDRIA ; ERC/KEYSTONE

The web and the social web play an increasingly important role as an information source for Members of Parliament and their assistants, journalists, political analysts and researchers. It provides important and crucial background information, like reactions to political events and comments made by the general public. The case study presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets an effective exploration of political web archives. In this paper, we describe semantic technologies deployed to ease the exploration of the archived web and social web content and present evaluation results.

Using a Web search engine is one of today's most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.

TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

This repository contains an expert-annotated dataset of 1261 tweets and the corresponding annotation framework from the publication "SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse" (https://arxiv.org/abs/2206.07360). The tweets are annotated with three different categories of science-relatedness:

(1) Scientific knowledge (scientifically verifiable claims): Tweets that include a claim or a question that could be scientifically verified, (2) Reference to scientific knowledge: Tweets that include at least one reference to scientific knowledge (references can either be direct, e.g., DOI, title of a paper or indirect, e.g., a link to an article that includes a direct reference), and (3) Related to scientific research in general: Tweets that mention a scientific research context (e.g., mention a scientist, scientific research efforts, research findings).

Further, the annotations include the annotators' confidence scores as well as labels for compound claims and ironic tweets.

ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics.

The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org). 

The latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ .

The first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations.

TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.

We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:

1. Tweet Id: Long.

2. Username: String. Encrypted for privacy issues.

3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).

4. #Followers: Integer.

5. #Friends: Integer.

6. #Retweets: Integer.

7. #Favorites: Integer.

8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".

9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").

10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".

11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".

12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"

To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.

You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .

ClaimsKG is a knowledge graph of metadata information for fact-checked claims scraped from popular fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonized and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model, query examples and statistics.

The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).

The latest release of ClaimsKG covers 74066 claims and 72127 Claim Reviews. This is the fourth release of the dataset where data was scraped till Jan 31, 2023 containing claims published between 1996 and 2023 from 13 fact-checking websites. The websites are Fullfact, Politifact, TruthOrFiction, Checkyourfact, Vishvanews, AFP (French), AFP, Polygraph, EU factcheck, Factograph, Fatabyyano, Snopes and Africacheck. The claim-review (fact-checking) period for claims ranges between the year 1996 to 2023. Similar to the previous release, the Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. Improvements have been made in the web scraping and data preprocessing pipeline to extract more entities from both claims and claims reviews. Currently, ClaimsKG contains 3408386 entities detected and referenced with DBpedia.

This latest release of ClaimsKG supersedes the previous versions as it contained all the claims from the previous versions together in addition to the additional new claims as well as improved entity annotation resulting in a higher number of entities.