71 research outputs found
Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives
How did the popularity of the Greek Prime Minister evolve in 2015? How did
the predominant sentiment about him vary during that period? Were there any
controversial sub-periods? What other entities were related to him during these
periods? To answer these questions, one needs to analyze archived documents and
data about the query entities, such as old news articles or social media
archives. In particular, user-generated content posted in social networks, like
Twitter and Facebook, can be seen as a comprehensive documentation of our
society, and thus meaningful analysis methods over such archived data are of
immense value for sociologists, historians and other interested parties who
want to study the history and evolution of entities and events. To this end, in
this paper we propose an entity-centric approach to analyze social media
archives and we define measures that allow studying how entities were reflected
in social media in different time periods and under different aspects, like
popularity, attitude, controversiality, and connectedness with other entities.
A case study using a large Twitter archive of four years illustrates the
insights that can be gained by such an entity-centric and multi-aspect
analysis.Comment: This is a preprint of an article accepted for publication in the
International Journal on Digital Libraries (2018
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Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned
Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions
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Verifying baselines for crisis event information classification on Twitter
Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability
Sentiment analysis during Hurricane Sandy in emergency response
Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself
Regional sentiment bias in social media reporting during crises
Crisis events such as terrorist attacks are extensively commented upon on social media platforms such as Twitter. For this reason, social media content posted during emergency events is increasingly being used by news media and in social studies to characterize the public’s reaction to those events. This is typically achieved by having journalists select ‘representative’ tweets to show, or a classifier trained on prior human-annotated tweets is used to provide a sentiment/emotion breakdown for the event. However, social media users, journalists and annotators do not exist in isolation, they each have their own context and world view. In this paper, we ask the question, ‘to what extent do local and international biases affect the sentiments expressed on social media and the way that social media content is interpreted by annotators’. In particular, we perform a multi-lingual study spanning two events and three languages. We show that there are marked disparities between the emotions expressed by users in different languages for an event. For instance, during the 2016 Paris attack, there was 16% more negative comments written in the English than written in French, even though the event originated on French soil. Furthermore, we observed that sentiment biases also affect annotators from those regions, which can negatively impact the accuracy of social media labelling efforts. This highlights the need to consider the sentiment biases of users in different countries, both when analysing events through the lens of social media, but also when using social media as a data source, and for training automatic classification models
Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events
This paper investigates bias in coverage between Western and Arab media on
Twitter after the November 2015 Beirut and Paris terror attacks. Using two
Twitter datasets covering each attack, we investigate how Western and Arab
media differed in coverage bias, sympathy bias, and resulting information
propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets
across four languages (English, Arabic, French, German), built a regression
model to characterize sympathy, and thereafter trained a deep convolutional
neural network to predict sympathy. Key findings show: (a) both events were
disproportionately covered (b) Western media exhibited less sympathy, where
each media coverage was more sympathetic towards the country affected in their
respective region (c) Sympathy predictions supported ground truth analysis that
Western media was less sympathetic than Arab media (d) Sympathetic tweets do
not spread any further. We discuss our results in light of global news flow,
Twitter affordances, and public perception impact.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann,
T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring,
Understanding, and Classifying News Media Sympathy on Twitter after Crisis
Events. In Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems (CHI '18). ACM, New York, NY, USA. DOI:
https://doi.org/10.1145/3173574.317413
Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events
This paper investigates bias in coverage between Western and Arab media on
Twitter after the November 2015 Beirut and Paris terror attacks. Using two
Twitter datasets covering each attack, we investigate how Western and Arab
media differed in coverage bias, sympathy bias, and resulting information
propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets
across four languages (English, Arabic, French, German), built a regression
model to characterize sympathy, and thereafter trained a deep convolutional
neural network to predict sympathy. Key findings show: (a) both events were
disproportionately covered (b) Western media exhibited less sympathy, where
each media coverage was more sympathetic towards the country affected in their
respective region (c) Sympathy predictions supported ground truth analysis that
Western media was less sympathetic than Arab media (d) Sympathetic tweets do
not spread any further. We discuss our results in light of global news flow,
Twitter affordances, and public perception impact.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann,
T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring,
Understanding, and Classifying News Media Sympathy on Twitter after Crisis
Events. In Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems (CHI '18). ACM, New York, NY, USA. DOI:
https://doi.org/10.1145/3173574.317413
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