Dipak K. Gupta

Government Coercion of Dissidents Deterrence or Provocation

The dynamic effect of government coercion on dissident activities has been a controversial issue. It is contended that this relationship is significantly altered when different control variables such as regime type, ideological orientation, and economic performance are employed. Time series data based on 24 countries is used to estimate the net effect of government coercion on two types of dissident activities: protest demonstrations and deaths from domestic group violence. It is shown that in democratic nations, government sanctions provoke a higher level of protest demonstrations. However, in nondemocratic countries, at the extreme, severe sanctions can impose an unbearable cost, resulting in an inverse relationship between sanctions and political deaths. The nature of the regime influences not only the dynamics of the relationship between government coercion and dissident activities, but also the qualitative character of opposition response.

'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

Suicide Bombing as a Strategic Weapon: An Empirical Investigation of Hamas and Islamic Jihad

Using twice-yearly data from 1991 to 2003, we analyze the incidents of suicide attacks by Hamas and Islamic Jihad within Israel and the Palestinian territories of the West Bank and the Gaza Strip. Given the exploratory nature of the question, we have first estimated the relevant coefficients by using a Quasi-Maximum Likelihood Ratio and then checked their robustness by reestimating the model with the help of a Seemingly Unrelated Regression (SUR) as an interrelated system. The results indicate that the two groups deliberately use suicide bombings as strategic weapons within the larger Israeli-Palestinian political milieu. With the Western world locked in an armed struggle with the militant extremists of Islam based on millenarian ideologies, this study emphasizes the need to develop appropriate analytical capabilities to distinguish among terrorist groups and their motivations, ideologies, and tactics. At times, Palestinian politics are dizzyingly incoherent,…at times bloody, at other times perfectly clear.—Edward W. Said

The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets

Background Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. Objective The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. Methods Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. Results Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. Conclusions This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field.

Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election

We introduce a new research framework for analyzing the spatial distribution of web pages and social media (Twitter) messages with related contents, called Visualizing Information Space in Ontological Networks (VISION). This innovative method can facilitate the tracking of ideas and social events disseminated in cyberspace from a spatial-temporal perspective. Thousands of web pages and millions of tweets associated with the same keywords were converted into visualization maps using commercial web search engines (Yahoo application programming interface (API) and Bing API), a social media search engine (Twitter APIs), Internet Protocol (IP) geolocation methods, and Geographic Information Systems (GIS) functions (e.g., kernel density and raster-based map algebra methods). We found that comparing multiple web information landscapes with different keywords or different dates can reveal important spatial patterns and “geospatial fingerprints” for selected keywords. We used the 2012 US Presidential Election candidates as our case study to validate this method. We noticed that the weekly changes of the geographic probability of hosting “Barack Obama” or “Mitt Romney” web pages are highly related to certain major campaign events. Both attention levels and the content of the tweets were deeply impacted by Hurricane Sandy. This new approach may provide a new research direction for studying human thought, human behaviors, and social activities quantitatively.

The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance

Background Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. Objective There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. Methods Tweets containing the keyword “flu” were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was “valid”, or from a user who was likely ill with the flu. Results Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data.

Group utility in the micro motivation of collective action: The case of membership in the AARP

Abstract The fundamental assumption of human rationality in neo-classical economic analysis is the maximization of individual utility. As economic analyses are being widely used in political science under the broad rubric of public choice, one cannot escape the logical inconsistency between theory and reality arising from the free-rider problem in the preference for collective goods. Recent literature argues that a way out of this dilemma is to recognize the existence of motivations other than self-interest. This paper demonstrates the need to enlarge the assumption of economic rationality by explicitly taking into account the factors of group utility. With the help of survey data on the preference for membership in the American Association of Retired People (AARP), this study adds to the mounting empirical evidence for the fact that for a proper understanding of collective action, the existence of a collective identity should be recognized.

Creating a composite index for assessing country performance in the field of human rights: proposal for a new methodology

This article sets out to provide a new methodology for attributing weights to the various indicators of human rights abuse. A number of studies have already collected data on various indicators of human rights abuse. These studies fall short, however, because they do not attribute weight to these indicators, and thus produce neither a composite indicator nor a group classification of countries according to their overall levels of performance. Relative weights can be attributed to these indicators in one of two ways. First, indicators can be weighted to reflect the values of the one constructing the index-an arbitrary scale. Alternatively, our methodology defines the extreme ends of the spectrum of human rights records by some widely acceptable standard, and then assesses the weights for the whole spectrum through Discriminant Analysis. Although this methodology will not end all the controversies on giving each human right indicator its relative place, nor rank individual countries on a world scale, this method is a step closer to a more objective measurement of human rights performance.

The Naxalites and the Maoist Movement in India: Birth, Demise, and Reincarnation

Far away from the glare of the achievements in the fields of information technology, a long festering Maoist insurgency is growing in the heart of India. The Maoists have found a strong base among the tribal people of India. By examining the history of Communist movements in India within a behavioral perspective, this article asks the question why in the past similar movements were relatively easy for the authorities to suppress, while the current Maoist insurgency is proving to be much harder to manage? 1 “The Naxalites occupy an ambiguous niche in history. Exemplary idealist to some, he indicates to others an expression of immature disaffection that has nothing constructive to offer. In either case, he embodies the reinstatement of man as a moral agent if only because Naxalites so radically challenge the premises of established morality.” — Rabindra Ray 2 (2002, p. 2–3).

Mapping ideas from cyberspace to realspace: visualizing the spatial context of keywords from web page search results

We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords. This research integrates GIScience and web-search engines to track and analyze public web pages and their web contents with associated spatial relationships. Web pages searched by clusters of keywords were mapped with real-world coordinates (by geolocating their Internet Protocol addresses). The resulting maps represent web information landscapes consisting of hundreds of populated web pages searched by selected keywords. By creating a Spatial Web Automatic Reasoning and Mapping System prototype, researchers can visualize the spread of web pages associated with specific keywords, concepts, ideas, or news over time and space. These maps may reveal important spatial relationships and spatial context associated with selected keywords. This approach may provide a new research direction for geographers to study the diffusion of human thought and ideas. A better understanding of the spatial and temporal dynamics of the ‘collective thinking of human beings’ over the Internet may help us understand various innovation diffusion processes, human behaviors, and social movements around the world.