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Government communications typically use social media for federal agencies, national policies, and emergency cases. However, it is crucial to understand the use of these platforms within local government agencies. This investigation fills this gap and includes social media data during three months of the COVID-19 pandemic and compares it with previous trends. The research question that leads this research is: How has government social media use and communication behavior changed due to the COVID-19 pandemic? This research analyzes a study case applied to a local Mexican government agency named ISSEMyM. We analyze the data during the period between October 2017 and June 2020. Findings reveal a consistent use of Twitter and Facebook for communication by the ISSEMyM government agency. Also, it indicates an increase in engagement and communication flow over three years. We found that official local government communications remained unchanged during the whole period. We found no change in interaction, number of followers, and message structure during this period;however, the COVID-19 pandemic radically alters interactions, followers, and user engagement. [ABSTRACT FROM AUTHOR] Copyright of Information Polity: The International Journal of Government & Democracy in the Information Age is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Affective polarization is a phenomenon that has invaded the political arena empowered by social networks. In this chapter, the authors analyze the Capitol riot posts on Twitter. To achieve this, the authors use affective computing introducing the multi-emotional charge combined with statistical analysis based on the t-student test and Welch's t-test. The research questions guiding this study are: How do social media platforms' messages impact on inciting? Do social media platforms' messages with negative emotional charge affect legitimizing of the Capitol protest? Findings identify the significant influence of Donald Trump on Twitter during the Capitol riot. Moreover, data analysis identifies positive and negative emotions towards Donald Trump as well as similarities in the showed emotions of Trump and the audience.

Living labs are settings for open innovation that offer a collaborative platform for research, development, and experimentation in real-life contexts. The research question is: What are the characteristics of AI projects developed in innovation labs for the public sector? The authors collected data from exploring the innovation labs in Latin America, Europe, and Asia. The selection of the laboratories leads to four cases: the Civic Laboratory of AI in the Netherlands, the Laboratorio de Gobierno in Chile (Government's Lab in Chile), Secretaría de Innovación y Transformación in Argentina (Innovation and Transformation Secretary in Argentina), and the Smart Government Innovation Lab in Hong Kong. This chapter comprises five sections beginning with an introduction describing the context of artificial intelligence in the public sector and innovation labs. The second section presents a literature review with similar studies and a conceptual framework. The third section describes the methodology, and the fourth reports on the main findings. The last section contains conclusions, limitations, and future research.

The aim of this article is to understand the way digital government laws are designed through the analysis and comparison of general objectives, governance mechanisms and roles assigned to stakeholders of six local state digital government laws in Mexico. Our findings reveal a strong degree of similarity between general objectives and implementation mechanisms in those six laws, showing a mix of traditional bureaucratic features and new public management features. We also find significant differences between those laws in the mobilization of behavior-constraining rules to fulfill e-government processes and solution-guiding mechanisms to foster innovation.

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

Open government has become a research trend among e-government scholars around the world. However, most research in this field has focused on national policies and the implementation thereof. Typically, this is related to Open Government (OG) ecosystems, data, policies and models, yet very little attention has been paid to the most basic level: the municipal/local level. The purpose of this research paper is to fill this gap. We based our research on an instrument for the assessment of OG priorities in Spanish municipalities (Gómez, Criado, & Gil-Garcia, 2017a). This instrument for assessment was adapted to Mexican municipalities. In so doing, we surveyed 67 cases of Mexican public officials working on OG or in transparency offices in municipalities across Mexico during 2017. This paper reports the findings of this exploratory research, grouping concepts of perception, and analyzing four elements of OG: 1. Open Government; 2. Transparency; 3. Citizen Participation and 4. Open Data. Going forward, we used factor analysis to group concepts and identify two principal perspectives for addressing OG policy implementation in local governments, one oriented at fostering technological innovation, and another at promoting democratic values of co-responsibility. Broadly, we discovered there is some emphasis on democratic values and some on technological innovation. We also found varying levels of knowledge on this topic in Mexican municipalities. Another finding is that OG policies are not well-defined due to a lack of resources for the promotion of genuine transparency, participation, and collaborative actions.

A recent trend in political campaign studies is the use of sentiment analysis to understand users’ decisions. The scandal of Facebook and Cambridge Analytics is an example of efforts to use social media platforms to impact citizens’ will. This research aims to answer the question: Did the Facebook reactions of users in Mexico reflect the outcomes of the elections and possibly also the users’ emotions toward the political candidates of the State of Mexico in 2017? To answer the research question, we analyzed data collected from 4,128 Facebook posts and their reactions. The available reactions for Facebook users are: like, love, haha, wow, sad, and angry. Doing so revealed some kind of mood from the users in the Facebook comments section and opinions of the local government campaign in the central State of Mexico. The elections studied took place in June 2017. Our findings show that the winning political party had more negative sentiment and fewer posts and users’ discussions of the candidates in Facebook comments sections than the political party with the largest positive sentiment. This party was unsuccessful in winning the elections.

Social media has transformed election campaigns around the world. While it is difficult to determine to what extent social media influence voters' decisions, there is no doubt that social media platforms impact on candidate advertising and public debate during elections. This research, the methodological formulation of which is based on a case study, seeks to investigate the use of social media during political campaigns to collect signatures of support. In the elections of 2018, aspiring candidates for presidential election required a certain number of signatures of support in order to register as official candidates. We collected social media data on a weekly basis from the Twitter, Facebook, and YouTube accounts of seven candidates and contrasted this data with the number of signatures validated by the electoral authority. We found no relationship between the level of support received and the use of social media in the case of any of the candidates. However, we observed candidates who did achieve the required number of signatures and who did receive official presidential candidate status as a result of their high level of visibility. This research contributes methodologically to the current literature and provides empirical evidence regarding independent candidates in Mexico.

In this chapter, the authors show two case studies of the use of social media in municipal governments: Lerma, a small municipality with a significant growth, and Metepec, an important municipality of the State of México. The purpose of this chapter is to provide empirical evidence of how social media improves government to citizen relationship and promotes e-participation in municipal governments. The results are based on semi-structured interviews applied to public servants and a survey to evaluate e-government services by citizens. So, the citizen perception is contrasted with public servants' interviews. Citizens consider that electronic procedures and services implemented by their municipalities do not generate value. The efforts of governments should focus on avoiding corruption, making governments transparent, opening data, and properly managing the privacy of information.

Social media has transformed election campaigns around the world. While it is difficult to determine to what extent social media influence voters' decisions, there is no doubt that social media platforms impact on candidate advertising and public debate during elections. This research, the methodological formulation of which is based on a case study, seeks to investigate the use of social media during political campaigns to collect signatures of support. In the elections of 2018, aspiring candidates for presidential election required a certain number of signatures of support in order to register as official candidates. We collected social media data on a weekly basis from the Twitter, Facebook, and YouTube accounts of seven candidates and contrasted this data with the number of signatures validated by the electoral authority. We found no relationship between the level of support received and the use of social media in the case of any of the candidates. However, we observed candidates who did achieve the required number of signatures and who did receive official presidential candidate status as a result of their high level of visibility. This research contributes methodologically to the current literature and provides empirical evidence regarding independent candidates in Mexico.

Una de las funciones de Twitter es el etiquetado digital denominado: hashtag. A través de las etiquetas, ordena y clasifica los mensajes de esta plataforma de redes sociales. El objetivo de esta investigación es estudiar el fenómeno del etiquetado social conocido como hashtag activism (activismo de etiquetas) como impulsores de la participación cívica, respondiendo la pregunta: ¿cómo entender la participación cívica online a través del etiquetado social en redes socio digitales? Nuestra metodología hace un análisis cualitativo de 33 casos representativos de las etiquetas #Ladies y #Lords utilizadas en México que realizan un escrutinio social desde el 2011. Concluimos que el etiquetado digital impacta en la participación cívica y la comunicación en la sociedad de la información.

Artificial intelligence has been applied to different sectors in the government from data analysis to predictive analytics, as well as, policing, combating the COVID-19 pandemic, and political election campaigns. In the academic field, the exploitation and understanding of data generated in social media have mostly focused on unimodal sentiment analysis, based on one-dimensional sentiment analysis. This research proposes an analysis of the emotional charge for the U.S. presidential elections in 2020, based on a hybrid approach that combines affective computing and classic statistical analysis. We analyze the multi-emotional charge of candidates and voters, as well as the potential relationship of the candidates' emotions on the voters. Through this analysis is possible to determine the degree of agreement between candidates and voters. Future research is proposed for the area of affective computing in political campaigns.

The Covid-19 pandemic happened when public administrations were convinced of the need to expose their data on open data portals. However, the global emergency that COVID-19 produced in governments highlighted some shortcomings in terms of public policies and strategies related to open data, open government, and open innovation. The aim of the open government and open data in times of COVID-19 panel is to discuss experiences and the potential consequences of open government and open data exploitation to combat the pandemic caused by the SARS-CoV-2 virus.

Open government implementation is usually top-down. It starts on the federal government, then goes to local governments, and finally arrives at the municipal or county level. Despite too much research on the national level, very little is known in the municipalities' efforts to implement open government. We intend to fill this gap by understanding the barriers that hinder the implementation of this effort. Our research question: What barriers have impeded the implementation of open government in Mexico's municipalities? To answer this question, we propose a combined assessment framework that include: government confidence (trust), regulations analysis, and open government barriers from the model of Zuiderwijk [39]. We surveyed 134 public managers from a sample of municipalities of the state of Mexico. Our findings show that the trust within government agencies comprises more precise regulations to foster open data, and open government is essential. Lack of innovation is a crucial barrier to implement open government at the municipal level in the state of Mexico.

Artificial Intelligence (IA) in the public sector takes the lead for public innovation. The recent government innovation development across the globe are focused on this topic. Some countries like China, France, Germany, Spain launched strategies for the next five or ten years to promote the development of iA in their countries' infrastructure, Human Resources, regulations, and ethics. This panel aims to understand the different strategies, their public policies, challenges and priorities, and the possible governance problems to be held.

Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.

This article presents a study about artificial intelligence (AI) policy based on the perceptions, expectations, and challenges/opportunities given by chief information officers (CIOs). In general, publications about AI in the public sector relies on experiences, cases, ideas, and results from the private sector. Our study stands out from the need of defining a distinctive approach to AI in the public sector, gathering primary (and comparative) data from different countries, and assessing the key role of CIOs to frame federal/national AI policies and strategies. This article reports three research questions, including three dimensions of analysis: (1) perceptions regarding to the concept of AI in the public sector; (2) expectations about the development of AI in the public sector; and, (3) challenges and opportunities of AI in the public sector. This exploratory study presents the results of a survey administered to federal/national ministerial government CIOs in ministries of Mexico and Spain. Our descriptive statistical (and exploratory) analysis provides an overall approach to our dimensions, exploratory answering the research questions of the study. Our data supports the existence of different governance models and policy priorities in different countries. Also, these results might inform research in this same area and will help senior officials to assess the national AI policies actually in process of design and implementation in different national/federal, regional/state, and local/municipal contexts.

The use of a simple categorization of emotions or even the use of universal expressions of emotions is unsuitable to properly identify sentiments in posts in some situations. The main goal of this paper is to analyze impressions of Twitter messages in the 19S Mexican earthquake of 2017 through machine learning techniques, specifically with classification algorithms. To identify impressions, we applied sentiment analysis based on supervised methods, and we identified a customized list of terms that we called impressions, which reflects the nature of tweets related to the event of study. Our proposed impressions analysis is useful to understand Twitter messages during different events since impressions adapt to each situation and context, based on emotional frameworks. We found that Twitter is useful to prove or disprove the information disseminated by the mass media and mainly for asking for help. Analyzing this kind of data in real-time will be useful for decision-making. The contribution of this paper is to fill the gap in the sentiment analysis area and the automatic identification of eleven impressions for disaster events in Twitter using machine learning techniques. This method has been called impression analysis.

Backgroud:Investors are always playing with the fears and desires of buyers and sellers. Stock exchange markets are not the exception. Financial sentiment analysis allows us to understand the effect of reactions and emotions on social media in the stock market. In this research, we analyze Twitter data and financial indices to answer the question: How do polarity generated by the posts on Twitter influence financial indices behavior in pandemic seasons? Methods:The study is based on the sentiment analysis of influential Twitter accounts in this field and its relationship with the behavior of important financial indices. To achieve this, we tested four lexicons to detect polarity on Twitter. Results:Our findings shows that the period in which the markets reacted was 6 to 13 days after the information was shared and disseminated on Twitter in the COVID-19 season, and 1 to 2 day for H1N1 season. Furthermore, in our analysis, we found that the lexicons that got the best results for sentiment analysis on Twitter were S140 and Affin.Conclusions:Financial sentiment analysis is an important technique to forecasting stock market and polarity is the most widely used technique in the financial area. There is a relationship between the polarity in Twitter and the financial indexes behavior. The most influential Twitter accounts during the pandemic season were The New York Times, Bloomberg, CNN News, and Investing, presenting a very high relation between sentiments on Twitter and the stock market behavior.

The pressure to adopt information technologies has clearly reached the judicial branch of government. General trends toward greater transparency and more openness in the public sector have pushed judges, ministers, and lawyers to use emergent technologies and to provide more information online. In fact, judicial agencies have created their own websites with diverse levels of technological sophistication and functionality, often with the intention of opening their processes and interacting with multiple stakeholders. In contrast to the executive branch, however, little is known about the structure, usability, content, and impacts of these websites. There is also no clear understanding of how judicial websites could be used to better understand and assess electronic justice and open justice efforts. Using a longitudinal mixed-method research design, this study analyzes the functionality of judicial websites and proposes a comprehensive assessment framework that not only evaluates the availability of information but also the participatory mechanisms related to e-justice and open justice. We started with an exploratory research project, followed by a pilot evaluation, and ended with an assessment of the 32 state judicial websites in Mexico in 2014 and 2016. Based on the results of these empirical efforts, this article proposes a framework to understand e-justice and open justice through the assessment of judicial websites.

Most of the research about how state governments use social media focuses on services, comparative perspectives or assessment of e-governments. The authors' focus is on the adoption of social media by state governments in Mexico. Many state governments may be slow to adopt social media due to a lack guidance on where their efforts may lead. The purpose of this research is to provide some insight into the factors affecting adoption of social media by state governments in Mexico. Guided by diffusion of innovation theory, the authors analyzed data from Twitter and Facebook accounts of all 32 Mexican state governments from 2010 to 2015. According to diffusion theory, the results find that only one state government in the sample is classified as an innovator, four state governments are early adopters, eleven are early majority, eleven are late majority, and five are laggards. The use of social media by state governments in Mexico is in its infancy, and there is a significant difference in the use of social media among the different states.

The purpose of this chapter is to describe the technological adoption by state governments, based on a longitudinal study of technology in Mexico for which the authors analyzed data from all the local governments from 2010 to 2018. With this data, they proposed a ranking to classify adoption technology, using the diffusion of the innovation theory. They included in the analysis other variables such as the percentage of households with a computer, internet, and other communication technology equipment. The results show that Mexico City is the innovator; Baja California, Sonora, and Nuevo Leon are early adopters, while Oaxaca, Chiapas, and Guerrero are laggards. The most influential variable in the adoption of information technologies is illiteracy, and there is an inverse relationship between technology and illiteracy. Future research will open several paths to understand different adoption behaviors between specific technologies in each state, such as big data, artificial intelligence, internet of things, and smart cities.

Political activism is more alive than ever. After the scandal of Facebook and Cambridge Analytica, online social media platforms restricted the distribution of content to privacy laws. But populism disruption in many countries fosters political discontent. Online protests and everyday claims are rising. Add to this context environmental problems and an absence of an ideological framework. All these conditions foster the use of digital activism. But this field of research has studied single cases, losing connections with societies and history. The aim of this chapter is to explain the evolution of digital activism in a long period of time. To achieve such purpose, the author analyzes 11 Mexican events that took place from 2000 to 2019 and provide a classification framework to understand how digital activism transforms over time.

Cloud computing is a revolutionary technology for businesses, governments, and citizens. Some examples of Software-as-a-Services (SaaS) of cloud computing are banking apps, e-mail, blog, online news, and social networks. In this chapter, we analyze data sets generated by trending topics on Twitter that emerged from Mexican citizens that interacted during the earthquake of September 19, 2017, using sentiment analysis and supervised learning, based on the Ekman’s six emotional model. We built three classifiers to determine the emotions of tweets that belong to the same topic. The classifiers with the best accuracy for predicting emotions were Naive Bayes and support vector machine. We found that the most frequent predicted emotions were happiness, anger, and sadness; also, that 6.5% of predicted tweets were irrelevant. We provide some recommendations about the use of machine learning techniques in sentiment analysis. Our contribution is the expansion of the emotions range, from three (negative, neutral, positive) to six in order to provide more elements to understand how users interact with social media platforms. Future research will include validation of the method with different data sets and emotions, and the addition of new artificial intelligence techniques to improve accuracy.

Artificial intelligence (AI) is the latest trend being implemented in the public sector. Recent advances in this field and the AI explosion in the private sector have served to promote a revolution for government, public service management, accountability, and public value. Incipient research to understand, conceptualize and express challenges and limitations is now ongoing. This paper is the first approach in such a direction; our research question is: What are the current AI trends in the public sector? In order to achieve that goal, we collected 78 papers related to this new field in recent years. We also used a public policy framework to identify future areas of implementation for this trend. We found that only normative and exploratory papers have been published so far and there are a lot of public policy challenges facing in this area, and that AI implementation results are unknown and unexpected; since there may be great benefits for governments and society, but, on the other hand, it may have negative results like the so-called ”algorithmic bias” of AI when making important decisions for social development. However, we consider that AI has potential benefits in the public health, public policies on climate change, public management, decision-making, disaster prevention and response, improving government-citizen interaction, personalization of services, interoperability, analyzing large amounts of data, detecting abnormalities and patterns, and discovering new solutions through dynamic models and simulation in real time.

Open Government (OG) is a term increasingly used in public administrations. The public managers seem to be interested in modernizing their models of public management and they find in the idea of OG strategies to respond to citizen demands. However, there seems to be a dispersion of what public managers understand as OG. This work is a comparative analysis of the perspectives that exists in the local governments of two countries: Mexico and Spain. An Exploratory Factor Analysis (EFA) is carried out to define the perspectives assumed by public managers in both countries and compare them. In order to achieve this purpose, we established the following research question: What perspectives about open government do local public managers have in Spain and Mexico? The findings indicate that while in Spain public managers configure three clear perspectives about OG, meanwhile in Mexico only two perspectives are identified.

The research question that leads this effort is: How do Twitter messages impact the polarization of emotions in the presidential election in Mexico in 2018? We collected 6,515 tweets posted by the five candidates that stood for the presidency of Mexico in 2018. We developed a methodology based on artificial intelligence techniques using machine learning and classifiers to predict the emotions of each candidate and the effects of those emotions on the other candidates. The results show that the candidate with the greatest polarization in the generated emotions won the electoral contest and that the emotions generated in Twitter affected other candidates.

The use of information technologies for the delivery of government services is core in the mission of government around the world. Although the promise of public value creation through digitizing government services is a recurrent theme in the literature, we still know little about the actual mechanisms of value creation from the citizen’s perspective. Understanding public value creation through digital service delivery is a complex and important research problem. Recent attempts to understand the impacts of electronic services value from the citizens’ perspective suggest that dividing service delivery in several stages could be a valuable approach to understand the different models of managing public service delivery, as well as the main sources of value to citizens and society. Moreover, the approach provides a model that can be used by public managers in the design, implementation, and evaluation of citizen-centered and value-based government services. We explore the usefulness of the framework through a series of focus groups with citizens in Mexico. As a result, we identified 3 families of services that follow trajectories with similar stages in the citizen government interaction, and we describe citizen’s perception of value from these conversations.

La relación entre democracia y corrupción ha sido difícil de descifrar. Muchas investigaciones se han realizado en torno a ello desde la cultura, la historia, la ciencia política y la teoría política. Sin embargo, esta reflexión parte de la existencia del uso de las tecnologías de información y comunicación y asu vez una nueva alternativa para configurar esta relación. Esta investigación propone la idea de que los datos abiertos impulsan la democratización de los datos y ello tiene un impacto en la reducción de la corrupción, de esta forma la pregunta de investigación sería: ¿Como los datos abiertos gubernamentales impulsan la democratización y su impacto en la reducción de la corrupción? A partir del marco teórico de la promulgación de las tecnologías para establecer una relación entre las instituciones y el uso de tecnologías, impulsando así prácticas como las de gobierno abierto. Para responder la interrogante he tomado el caso de México que recientemente ha impulsado políticas de apertura de datos a partir del gobierno abierto. Se ha demostrado que el uso del gobierno electrónico tiene un impacto favorable en la reducción de la corrupción, y las políticas de gobierno abierto se vinculan fuertemente con las ideas de democratización de la información. El objetivo de esta investigación es explicar esta posición a partir de las prácticas de gobierno abierto en México.

Public access to accurate and reliable information is fundamental to democracy and democratic decision-making. In emerging democracies, during political crises or controversial elections, reliable information is often difficult to obtain. In authoritarian regimes, citizens' perceptions of information reliability are tightly linked to trust in government, and both are typically low. Open government policies help build trust that is essential during protests or contentious elections. Uses and gratifications (U&G) theory states that people use diverse media to try to satisfy their need for reliable information. Gratifying this need is tied to the larger concept of political information efficacy (PIE), i.e., the belief a person holds about him/herself as being politically informed and competent to participate in political thought and action. We investigated these inter-related concepts by administering a survey to an opportunity sample of young adults at a public state university in Mexico. We find that respondents perceived that information from face-to-face and telephone communication with members of their social network, as well as online sources were the most reliable. Information from offline sources, such as TV and newspapers, especially pro-government sources, were the least reliable. Further, the information factors of availability, reliability, influence and sharing had positive relationships with PIE. These results suggest that in conditions of uncertain political information, social networks and online information sources are essential to political information efficacy and democratic political participation. Categories and subject descriptors H.4.3 [Information Systems Applications]: Communications Applications (e.g., email, information browsers, bulletin boards). General terms Measurement, Reliability, Experimentation, Human Factors

La influencia de la tecnología en la administración pública ha marcado una nueva etapa para los gobiernos del mundo. La estrategia conocida como Gobierno Electrónico, ha impactado en distinta medida la forma de organizar las oficinas públicas, la relación con los ciudadanos y la estructura de la administración pública en sus distintos niveles. Sin embargo, este impacto no se ha medido suficientemente y existe escasa evidencia a este respecto. Uno de los elementos más innovadores en esta transformación del gobierno es el uso de redes sociales. El impacto de estas plataformas – como Twitter y Facebook– en la comunicación y operaciones del gobierno ha dado un giro en la manera en que las Tecnologías de Información y Comunicación han modificado la relación ciudadano-gobierno. El objetivo de este artículo es entender qué tanto se utilizan dichas plataformas en los 32 portales estatales mexicanos y observar de qué forma ha evolucionado su uso entre 2010 y 2011.

Social media has invaded elections in Mexico. However, the power of citizens through the use of this platform is still unknown. Many citizens criticize political candidates using Twitter, others build networks and some others try to collaborate with candidates. This research is focused in understanding this kind of behavior, analyzing the case of the presidential candidate Enrique Peña Nieto (PRI) in Mexico who won the presidency with a large participation but without the support of Twitter users. After two online protests against this presidential candidate - #IamnotProletariat and #Iam132 – political image could have been undermined and voters could have thought differently. But this was not the case and despite of this, the candidate won. The challenge to understand this online protest and its link to the political campaign is addressed in this paper.

Political messaging is adapting to new digital spaces. However, the power of citizens through the use of this digital spaces is still unknown. Many citizens criticize political candidates using Facebook or Twitter, others build networks in Snapchat and some others try to collaborate with candidates using Periscope or WhatsApp. This research is focused in understanding this adaptation of political message on this platforms, analyzing the case of the presidential candidate Enrique Peña Nieto (PRI) in Mexico who won the presidency with a large participation but without the support of Twitter users. After two online protests against this presidential candidate - #IamnotProletariat and #Iam132 – political image could have been undermined and voters could have thought differently. But this was not the case and despite of this, the candidate won. The challenge to understand this online protest and its link to the political message is addressed in this paper.

The purpose of this paper is to conceptualize Artificial Intelligence and applications used by the government through a meta-analysis to identify definitions, techniques, and applications for government. We found that Artificial Intelligence is useful for the decision-making process, cost reduction, changes in the environment, biomedics, disaster prevention and response, education, and personalize services among other applications.

Open government is a worldwide trend. Many countries started implementation process a few years ago in different topics such as: information access, transparency, accountability and open data. However, this process has different outcomes and meanings related to the Open Government concept. The purpose of this panel is to discuss the use of the feasibility of the use of MOOCS in the virtual education program. Panelists use the example of the Transnational Open Government Virtual Education Project, sponsored by ERASMUS and promoted by the Spanish social network NovaGob. Some questions of discussion will address topics such as: government culture, MOOCS and virtual education challenges; open government education programs for bureaucracy.

The use of social media platforms in political campaigns has widely been studied. Many scholars have provided evidence about the impact of Twitter, Facebook and YouTube. So far; some of them have argued on the uselessness of these technologies about changing vote decisions, candidates' perception or political information efficacy. Most recently, the sentiment analysis has provided new paths to understand the link between the social media technology and voters. This paper analyzes data collected from Facebook's posts (4128 posts) and their emoticons - love, hate, anger, likes, etc. - that reveals some kind of sentiment from the users in a local government campaign in the central State of Mexico, which took place in June 2017. Findings show that political parties have a large impact with few posts, but in general, this paper reveals that voters' perception of candidates in Facebook is bad for the winner political party, since despite of this situation, the political party with the best sentiment impact, could not win the elections.

The purpose of this research is to provide some understanding of Twitter Networks, using the data from the Twitter discussion generated on the State of the Union from Governor Eruviel Avila in 2015 and 2016. We analyzed and compared Network Links from tweets of two years, using Netlytic as a mining text tool. This research presents two contributions: 1) the links analysis perspective of social media; and, 2) we proposes a methodology to assess the impact of online social media and government promotion. Our findings suggest that citizens use social media platforms to interact with politicians in offices, and supports the argument about “networked individualism” in which analyzed Twitter accounts reveal many citizens’ opinions and retweets related to the governor’s use of YouTube for his State of the Union speech. Decision-makers can use this study to improve communication with their customers (public) and allocate resources effectively for better public services. Finally, the last trend has tried to understand content analysis by wording. There is a lack of research about network links, their quality and users that are part of such network to understand in an integrated perspective the impact of social media.

Open government, as a concept, should be understood through a holistic perspective. It is not enough to provide access to government information without redesigning processes and changing regulations. However, little work has been done to measure the components of open government and provide guidance for implementation. We argue that one way to start filling this gap is through the development of models that integrate concepts, theories, and practical perspectives. However, very few of the existing models attempt to explain the functions and characteristics of open government, which could then provide guidance for its development. This article is based on two main sources in order to propose an integrative open government assessment model: (1) a literature review of previous assessment models and theoretical perspectives to build a richer integrative model that goes beyond websites and (2) the results from a longitudinal research study performed from 2006 to 2012 using a multicomponent model to me...

Social media has invaded elections in Mexico. However, the power of citizens through the use of this platform is still unknown. Many citizens criticize political candidates using Twitter, others build networks and some others try to collaborate with candidates. This research is focused in understanding this kind of behavior, analyzing the case of the presidential candidate Enrique Peña Nieto (PRI) in Mexico who won the presidency with a large participation but without the support of Twitter users. After two online protests against this presidential candidate - #IamnotProletariat and #Iam132 – political image could have been undermined and voters could have thought differently. But this was not the case and despite of this, the candidate won. The challenge to understand this online protest and its link to the political campaign is addressed in this paper.

Part 7: Evaluation; International audience; Understanding public value creation through electronic services is a complex and important research problem. Recent attempts to understand electronic services value from the citizen perspective suggest that dividing service delivery in several stages could be a valuable approach to understand ways in which information technologies support value creation when providing electronic services. Therefore, we propose the use of this process model as a tool to analyze and define public value creation through electronic services. We show the potential value of the model using birth certificate requests as a hypothetical example. We conclude the paper by describing how we are applying the model to our current research.

There are different measurements and rankings in the literature that evaluate critical factors for a successful adoption of digital government. Each of these frameworks applies diverse variables and approaches to assessing digital government success. However, the nature of digital government success is complex and implies multiple dimensions that make it difficult to evaluate in practice. There is the need to build more robust approaches to improve these assessments to explore the multiple dimensions of digital government success. Some authors have proposed factor analysis techniques as a useful tool for this task. Using a well-known ranking of state government portals in Mexico during the period 2009-2015, this study conducted a confirmatory factor analysis to evaluate the dimensions of this instrument, which includes 132 items. This ranking could be considered a typical tool for measuring and assessing digital government, similar to the ones used in many countries and by some international organizations. The purpose of this study is to extend our understanding of the multiple dimensions of digital government success and to provide guidance for improving and refining existing techniques for measuring and assessing digital government. The results ratify most of the original dimensions, but allows reducing the number of questions and obtain more robust estimations. Based on the analysis, we provide a set of practical recommendations for improving measurement methodologies and for the assessment of digital government.