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Author

Venkata Rama Kiran Garimella

Other affiliations: Aalto University
Bio: Venkata Rama Kiran Garimella is an academic researcher from Qatar Computing Research Institute. The author has contributed to research in topics: Social media & Page view. The author has an hindex of 9, co-authored 11 publications receiving 423 citations. Previous affiliations of Venkata Rama Kiran Garimella include Aalto University.

Papers
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Proceedings ArticleDOI
07 Apr 2014
TL;DR: Geolocated Twitter data can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting, and can substantially improve the understanding of the relationships between internal and international migration.
Abstract: Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.

191 citations

Proceedings ArticleDOI
25 Aug 2013
TL;DR: This work uses public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter and provides a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.
Abstract: We use public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter. Starting with a set of prominent seed Twitter users from both camps, we follow retweeting edges to obtain an extended network of users with inferred political orientation. We present an in-depth description of the members of the two camps, both in terms of behavior on Twitter and in terms of offline characteristics such as gender. Through the identification of partisan users, we compute a valence on the secular vs. Islamist axis for hashtags and use this information both to analyze topical interests and to quantify how polarized society as a whole is at a given point in time. For the last 12 months, large values on this "polarization barometer" coincided with periods of violence. Tweets are furthermore annotated using hand-crafted dictionaries to quantify the usage of (i) religious terms, (ii) derogatory terms referring to other religions, and (ii) references to charitable acts. The combination of all the information allows us to test and quantify a number of stereo-typical hypotheses such as (i) that religiosity and political Islamism are correlated, (ii) that political Islamism and negative views on other religions are linked, (iii) that religiosity goes hand in hand with charitable giving, and (iv) that the followers of the Egyptian Muslim Brotherhood are more tightly connected and expressing themselves "in unison" than the secular opposition. Whereas a lot of existing literature on the Arab Spring and the Egyptian Revolution is largely of qualitative and descriptive nature, our contribution lies in providing a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.

124 citations

Proceedings ArticleDOI
07 May 2016
TL;DR: It is found that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics, and hints at the potential of using machine- generated tags to study substance abuse.
Abstract: Several projects have shown the feasibility to use emph{textual} social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged emph{images} from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuable for studying public health. We find that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics. Whereas for most statistics user-provided tags are better features, for predicting excessive drinking machine-generated tags such as "liquid' and "glass' yield better models. This hints at the potential of using machine-generated tags to study substance abuse.

71 citations

Proceedings Article
01 Jan 2017
TL;DR: In this paper, the authors analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their network, including how people follow political and media accounts, their tweeting behavior, and how partisan the hashtags they use.
Abstract: Social media has played an important role in shaping political discourse over the last decade. At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network — how people follow political and media accounts, (ii) tweeting behavior — whether they retweet content from both sides, and (iii) content — how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10% - 20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.

45 citations

Book ChapterDOI
24 Mar 2013
TL;DR: Political Hashtag Trends (PHT) is an analysis tool for political left-vs-right polarization of Twitter hashtags, giving insights into the polarizing U.S. American issues on Twitter.
Abstract: Political Hashtag Trends (PHT) is an analysis tool for political left-vs.-right polarization of Twitter hashtags. PHT computes a leaning for trending, political hashtags in a given week, giving insights into the polarizing U.S. American issues on Twitter. The leaning of a hashtag is derived in two steps. First, users retweeting a set of "seed users" with a known political leaning, such as Barack Obama or Mitt Romney, are identified and the corresponding leaning is assigned to retweeters. Second, a hashtag is assigned a fractional leaning corresponding to which retweeting users used it. Non-political hashtags are removed by requiring certain hashtag co-occurrence patterns. PHT also offers functionality to put the results into context. For example, it shows example tweets from different leanings, it shows historic information and it links to the New York Times archives to explore a topic in depth. In this paper, we describe the underlying methodology and the functionality of the demo.

35 citations


Cited by
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Journal ArticleDOI
01 Jan 2017
TL;DR: A comprehensive up-to-date review of research employing deep learning in health informatics is presented, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook.
Abstract: With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

1,367 citations

Journal ArticleDOI
11 Jul 2019
TL;DR: A framework for identifying a broad range of menaces in the research and practices around social data is presented, including biases and inaccuracies at the source of the data, but also introduced during processing.
Abstract: Social data in digital form—including user-generated content, expressed or implicit relations between people, and behavioral traces—are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding “what the world thinks” about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naive usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. “For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin1

379 citations

Proceedings ArticleDOI
21 Jul 2017
TL;DR: This paper introduces Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images, and demonstrates that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic.
Abstract: In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general. Code, data and models are publicly available

346 citations

Journal ArticleDOI
01 Nov 2018
TL;DR: It is shown that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it.
Abstract: Automatic Gender Recognition (AGR) is a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos. In wider society the technology has proposed applications in physical access control, data analytics and advertising. Within academia, it is already used in the field of Human-Computer Interaction (HCI) to analyse social media usage. Given the long-running critiques of HCI for failing to consider and include transgender (trans) perspectives in research, and the potential implications of AGR for trans people if deployed, I sought to understand how AGR and HCI understand the term "gender", and how HCI describes and deploys gender recognition technology. Using a content analysis of papers from both fields, I show that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it. In addition, I use the dearth of discussion of this in HCI papers that apply AGR to discuss how HCI operationalises gender, and the implications that this has for the field's research. I conclude with recommendations for alternatives to AGR, and some ideas for how HCI can work towards a more effective and trans-inclusive treatment of gender.

275 citations

Journal ArticleDOI
TL;DR: The authors investigate the role of homophily in the diffusion of political information in social networks and develop a model predicting disproportionate exposure to like-minded information and that larger groups have more connections and are exposed to more information.

260 citations