Geoparsing and Geosemantics for Social Media

Middleton, Stuart E.; Krivcovs, Vadims

doi:10.1145/2842604

Cited by 25 publications

(4 citation statements)

References 18 publications

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“…And some research on the credibility of users [61,62] and some others try to determine coordinated users who behave together and manipulate data content [63]. And studies claim social media is full of rumors and most part of the accountholders spreading wrong information while there are emergencies and do not correct the content even if they are informed later [64,65]. In respect to this, social media data requires to be assessed without removing any data but accepting all these deficits and considering them with the nature of itself, since it is not possible to control each user credibility in real-time without historical data or demographic information.…”

Section: Discussionmentioning

confidence: 99%

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Gulnerman

Karaman

Pekaslan

et al. 2020

IJGI

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Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.applications and is referred to as Volunteered Geographic Information (VGI) [4,5]. The users can be thought of as unconscious volunteers for social media (SM) VGI, as deliberate volunteers for peer production VGI and as public participators for in citizen science based VGI [6][7][8]. The way of producing these forms of VGI is referred to as neo-geography in that it adopts neo-geographers (i.e., volunteers) who contributes to mapping activity without being expert [9]. This inexperience with regards to data production is questioned in the context of data quality [10][11][12], demographic bias (such as, gender, socioeconomic and educational aspects) [13,14] sampling bias (referring to volunteer sampling) and its impact on the generated data [15,16].In their very first form, citizen science projects in the very first forms were carried out with the use of paper maps [1]. However, with the...

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Section: Discussionmentioning

confidence: 99%

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Gulnerman

Karaman

Pekaslan

et al. 2020

IJGI

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“…Twitter) and analyze the best algorithms for jointly estimating the real location of Twitter events. The scalable architecture used for geoparsing and geosemantics extraction in the EU REVEAL project [13] included features like the tweet content, position of the terms, part-of-speech (POS) sets, and 3-gram feature sets that combined named entities with their POS tags. It is clear that the key to obtaining good results is to focus on continuously improving the NEL pipelines.…”

Section: Named Entity Linking Systemsmentioning

confidence: 99%

Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Weichselbraun

Kuntschik

Brașoveanu

2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

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Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.

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“…Through two-way transmission, the users become both the receivers and the creators of new media content. Some studies have suggested that users are attached to their personal preferences, opinions, and relative personal influence when they create new media content [7][8]. This has ensured that new media content in the transmission process allows users can form a certain ship network and become an essential part of the new media time.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Media Content Recommendation in the New Media Era Considering Scenario Clustering Algorithm

Tian

2024

IJCAI

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In the era of new media, the abundance of internet information poses a difficulty for users to find media that is both relevant and captivating. Although recommending technologies has made significant progress, it still faces hurdles in dealing with concerns related to data confidentiality and, the algorithmic partiality effect. With the continuous progress of the social economy, new media and micro media are constantly emerging in multiple ways and the methods to access these media contents have become diversified as well. However, it should be noted that diverse types of media content in the era of big data also require excessive time spent in selecting effective content. In response to these demands and defects, a scenario clustering algorithm is introduced in this paper, in which the media content recommendation is taken as the breakthrough point to build a clustering model to express the effective distribution of events by analyzing the network structure and media content distribution model through the analysis of the network structure and the distribution of the media content to represent the effective distribution of events and carry out the comparison of cross-content events, to achieve the effective clustering and analysis of media content. The results of the simulation experiment indicate that the scenario clustering algorithm proposed in this paper is effective and can support the analysis of media content recommendation in multiple dimensions, to provide high-quality media.Povzetek: Predstavljen je algoritem za gručenje scenarijev za izboljšanje priporočil medijskih vsebin, ob upoštevanju zasebnosti in algoritmične pristranskosti, z analizo mrežne strukture.

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Geoparsing and Geosemantics for Social Media

Cited by 25 publications

References 18 publications

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Citizens’ Spatial Footprint on Twitter—Anomaly, Trend and Bias Investigation in Istanbul

Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Analysis of Media Content Recommendation in the New Media Era Considering Scenario Clustering Algorithm

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