| 1. |
- Lincke, Alisa, 1989-, et al.
(författare)
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Diabetes Information in Social Media
- 2018
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Ingår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18). - New York, NY, USA : ACM Publications. - 9781450365017 ; , s. 104-105
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Konferensbidrag (refereegranskat)abstract
- Social media platforms have created new ways for people to communicate and express themselves. Thus, it is important to explore how e-health related information is generated and disseminated in these platforms. The aim of our current efforts is to investigate the content and flow of information when people in Sweden use Twitter to talk about diabetes related issues. To achieve our goals, we have used data mining and visualization techniques in order to explore, analyze and cluster Twitter data we have collected during a period of 10 months. Our initial results indicate that patients use Twitter to share diabetes related information and to communicate about their disease as an alternative way that complements the traditional channels used by health care professionals.
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| 2. |
- Lundberg, Jonas, 1964-, et al.
(författare)
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Towards a language independent Twitter bot detector
- 2019
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Ingår i: Proceedings of 4th Conference of The Association Digital Humanities in the Nordic Countries. - Copenhagen : University of Copenhagen. ; , s. 308-319
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Konferensbidrag (refereegranskat)abstract
- This article describes our work in developing an application that recognizes automatically generated tweets. The objective of this machine learning application is to increase data accuracy in sociolinguistic studies that utilize Twitter by reducing skewed sampling and inaccuracies in linguistic data. Most previous machine learning attempts to exclude bot material have been language dependent since they make use of monolingual Twitter text in their training phase. In this paper, we present a language independent approach which classifies each single tweet to be either autogenerated (AGT) or human-generated (HGT). We define an AGT as a tweet where all or parts of the natural language content is generated automatically by a bot or other type of program. In other words, while AGT/HGT refer to an individual message, the term bot refers to non-personal and automated accounts that post content to online social networks. Our approach classifies a tweet using only metadata that comes with every tweet, and we utilize those metadata parameters that are both language and country independent. The empirical part shows good success rates. Using a bilingual training set of Finnish and Swedish tweets, we correctly classified about 98.2% of all tweets in a test set using a third language (English).
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| 3. |
- Alissandrakis, Aris, 1975-, et al.
(författare)
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Visualizing dynamic text corpora using Virtual Reality
- 2018
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Ingår i: ICAME 39 : Tampere, 30 May – 3 June, 2018. - Tampere : University of Tampere. ; , s. 205-205
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Konferensbidrag (refereegranskat)abstract
- In recent years, data visualization has become a major area in Digital Humanities research, and the same holds true also in linguistics. The rapidly increasing size of corpora, the emergence of dynamic real-time streams, and the availability of complex and enriched metadata have made it increasingly important to facilitate new and innovative approaches to presenting and exploring primary data. This demonstration showcases the uses of Virtual Reality (VR) in the visualization of geospatial linguistic data using data from the Nordic Tweet Stream (NTS) project (see Laitinen et al 2017). The NTS data for this demonstration comprises a full year of geotagged tweets (12,443,696 tweets from 273,648 user accounts) posted within the Nordic region (Denmark, Finland, Iceland, Norway, and Sweden). The dataset includes over 50 metadata parameters in addition to the tweets themselves.We demonstrate the potential of using VR to efficiently find meaningful patterns in vast streams of data. The VR environment allows an easy overview of any of the features (textual or metadata) in a text corpus. Our focus will be on the language identification data, which provides a previously unexplored perspective into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region.Our VR prototype utilizes the HTC Vive headset for a room-scale VR scenario, and it is being developed using the Unity3D game development engine. Each node in the VR space is displayed as a stacked cuboid, the equivalent of a bar chart in a three-dimensional space, summarizing all tweets at one geographic location for a given point in time (see: https://tinyurl.com/nts-vr). Each stacked cuboid represents information of the three most frequently used languages, appropriately color coded, enabling the user to get an overview of the language distribution at each location. The VR prototype further encourages users to move between different locations and inspect points of interest in more detail (overall location-related information, a detailed list of all languages detected, the most frequently used hashtags). An underlying map outlines country borders and facilitates orientation. In addition to spatial movement through the Nordic areas, the VR system provides an interface to explore the Twitter data based on time (days, weeks, months, or time of predefined special events), which enables users to explore data over time (see: https://tinyurl.com/nts-vr-time).In addition to demonstrating how the VR methods aid data visualization and exploration, we will also briefly discuss the pedagogical implications of using VR to showcase linguistic diversity.
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| 4. |
- Alissandrakis, Aris, 1975-, et al.
(författare)
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Visualizing rich corpus data using virtual reality
- 2019
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Ingår i: Studies in Variation, Contacts and Change in English. - Helsinki : VARIENG. - 1797-4453. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- We demonstrate an approach that utilizes immersive virtual reality (VR) to explore and interact with corpus linguistics data. Our case study focuses on the language identification parameter in the Nordic Tweet Stream corpus, a dynamic corpus of Twitter data where each tweet originated within the Nordic countries. We demonstrate how VR can provide previously unexplored perspectives into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region and showcase its geospatial variation. We utilize a head-mounted display (HMD) for a room-scale VR scenario that allows 3D interaction by using hand gestures. In addition to spatial movement through the Nordic areas, the interface enables exploration of the Twitter data based on time (days, weeks, months, or time of predefined special events), making it particularly useful for diachronic investigations.In addition to demonstrating how the VR methods aid data visualization and exploration, we briefly discuss the pedagogical implications of using VR to showcase linguistic diversity. Our empirical results detail students’ reactions to working in this environment. The discussion part examines the benefits, prospects and limitations of using VR in visualizing corpus data.
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| 5. |
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| 6. |
- Hedenborg, Mathias, et al.
(författare)
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A Framework for Memory Efficient Context-Sensitive Program Analysis
- 2022
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Ingår i: Theory of Computing Systems. - : Springer. - 1432-4350 .- 1433-0490. ; 66, s. 911-956
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Tidskriftsartikel (refereegranskat)abstract
- Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking.In this paper we propose χ-terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. χ-terms are implemented as directed acyclic graphs without any redundant subgraphs. We introduce the k-approximation and the l-loop-approximation that limit the size of the context-sensitive information at the cost of analysis precision. We prove that every context-insensitive data-flow analysis has a corresponding k, l-approximated context-sensitive analysis, and that these analyses are sound and guaranteed to reach a fixed point.We also present detailed algorithms outlining a compact, redundancy-free, and DAG-based implementation of χ-terms.
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| 7. |
- Hedenborg, Mathias, et al.
(författare)
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Memory efficient context-sensitive program analysis
- 2021
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Ingår i: Journal of Systems and Software. - : Elsevier. - 0164-1212 .- 1873-1228. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking.In this paper we propose χ-terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. χ-terms are implemented as directed acyclic graphs without any redundant subgraphs.To show the efficiency of our approach we run experiments comparing the memory usage of χ-terms with four alternative data structures. Our experiments show that χ-terms clearly outperform all the alternatives in terms of memory efficiency.
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| 8. |
- Laitinen, Mikko, 1973-, et al.
(författare)
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ELF, language change and social networks : Evidence from real-time social media data
- 2020
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Ingår i: Language Change. - Cambridge : Cambridge University Press. - 9781108729819 ; , s. 179-204
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Bokkapitel (refereegranskat)abstract
- This article extends ELF studies towards variationist and computational sociolinguistics. It uses social network theory to explore how ELF is embedded in the social structures in which it is used and explores the size and nature of social networks in ELF. The empirical part investigates if multilingual and often mobile ELF users have larger networks and more weak ties than others, and if they therefore could be more likely to act as innovators or early adopters of change than the other speaker groups. Our empirical material consists of real-time social media data from Twitter. The results show that, statistically speaking, social embedding of ELF creates conditions that favor change. ELF users have larger networks and more weak ties than the other groups examined here. With regard to methods, social embedding needs to be taken into account in future studies, and we illustrate that variationist and computational sociolinguistics offers a useful theoretical and methodological toolbox for this task.
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| 9. |
- Laitinen, Mikko, 1973-, et al.
(författare)
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Revisiting weak ties : Using present-day social media data in variationist studies
- 2017
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Ingår i: Exploring Future Paths for Historical Sociolinguistics. - Amsterdam : John Benjamins Publishing Company. - 9789027200860 ; , s. 303-325
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Bokkapitel (refereegranskat)abstract
- This article makes use of big and rich present-day data to revisit the social network model in sociolinguistics. This model predicts that mobile individuals with ties outside a home community and subsequent loose-knit networks tend to promote the diffusion of linguistic innovations. The model has been applied to a range of small ethnographic networks. We use a database of nearly 200,000 informants who send micro-blog messages in Twitter. We operationalize networks using two ratio variables; one of them is a truly weak tie and the other one a slightly stronger one. The results show that there is a straightforward increase of innovative behavior in the truly weak tie network, but the data indicate that innovations also spread under conditions of stronger networks, given that the network size is large enough. On the methodological level, our approach opens up new horizons in using big and often freely available data in sociolinguistics, both past and present.
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| 10. |
- Laitinen, Mikko, 1973-, et al.
(författare)
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Size matters : digital social networks and language change
- 2020
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 3, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Social networks play a role in language variation and change, and the social network theory has offered a powerful tool in modeling innovation diffusion. Networks are characterized by ties of varying strength which influence how novel information is accessed. It is widely held that weak-ties promote change, whereas strong ties lead to norm-enforcing communities that resist change. However, the model is primarily suited to investigate small ego networks, and its predictive power remains to be tested in large digital networks of mobile individuals. This article revisits the social network model in sociolinguistics and investigates network size as a crucial component in the theory. We specifically concentrate on whether the distinction between weak and strong ties levels in large networks over 100 nodes. The article presents two computational methods that can handle large and messy social media data and render them usable for analyzing networks, thus expanding the empirical and methodological basis from small-scale ethnographic observations. The first method aims to uncover broad quantitative patterns in data and utilizes a cohort-based approach to network size. The second is an algorithm-based approach that uses mutual interaction parameters on Twitter. Our results gained from both methods suggest that network size plays a role, and that the distinction between weak ties and slightly stronger ties levels out once the network size grows beyond roughly 120 nodes. This finding is closely similar to the findings in other fields of the study of social networks and calls for new research avenues in computational sociolinguistics.
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