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Comunicar Journal 55: The Media Sphere. Controversies in Public Life (Vol. 26 - 2018)

#TrumpenMexico. Transnational connective action on Twitter and the border wall dispute


Meneses, María-Elena

Martín-del-Campo, Alejandro

Rueda-Zárate, Héctor


This article aims to identify how digital public opinion was articulated on Twitter during the visit of the Republican presidential candidate Donald Trump to Mexico City in 2016 by invitation from the Mexican government, which was preceded by the threat to construct a border wall that Mexico would pay for. Using a mixed methodology made up of computational methods such as data mining and social network analysis combined with content analysis, the authors identify conversational patterns and the structures of the net-works formed, beginning with this event involving the foreign policy of both countries that share a long border. The authors study the digital media practices and emotional frameworks these social network users employed to involve themselves in the controversial visit, marked by complex political, cultural and historical relations. The analysis of 352,203 tweets in two languages (English and Spanish), those most used in the conversations, opened the door to an understanding as to how transnational public opinion is articulated in connective actions detonated by newsworthy events in distinct cultural contexts, as well as the emotional frameworks that permeated the conversation, whose palpable differences show that Twitter is not a homogeneous universe, but rather a set of universes co-determined by sociocultural context.


Networks, social networks, public opinion, political communication, digital communication, cultural practices, emotions, virtual environment

English PDF file

Spanish PDF file

1. Introduction

“Donald Trump to visit Mexico after more than a year of Mocking It”, The New York Times front page announced on August 31, 2016 (Corasaniti & Ahmed, 2016). Candidate Donald Trump’s visit to Mexico, by invitation of President Enrique Peña Nieto, was considered an act of the Mexican government’s clumsiness by the international press (The Economist, 2016; Ahmed & Malkin, 2016), due to Trump’s disparaging comments, threatening throughout his campaign to build a border wall that would be paid for by Mexico. On the night of August 30, 2016, Mexicans learned from a tweet by the American candidate that Peña had invited him to visit the country. Twitter was the protagonist of the event since the Mexican primetime newscasts were getting off the air. Trump’s tweet was ratified by the Mexican presidency, and The Washington Post announced the information as breaking news. These were the sources of information available to the Mexican digital media, which were able to cover the eleventh-hour meeting, and the only input for a connective action to begin to be articulated on Twitter.

Bennett and Segerberg (2013) call “connective action” those various kinds of movements organized through networks whose flexibility facilitates participation in political life and constitutes the theoretical starting point of this research. To understand how public opinion was articulated during this connective action on Twitter, the authors identify patterns, actors, media practices and emotional frameworks with which users made sense of their agency with relation to this episode of Mexico–United States politics.

The following questions are posed: How was the connective action articulated by Trump’s visit? Who were the most influential actors, which were their communities, and what media practices facilitated their preeminence in this connective action? What emotional frameworks were used to make sense of the connective action?

The analysis corresponds to a period of four days: the day the visit was announced, the day of the visit, and the two following days in which the issue continued to be discussed.

1.1. Connective action and emotional frameworks

Before the visit, a reactive and affective (Papacharissi & Oliveira, 2012) reaction was articulated, as is characteristic of the Twitter public (Jungherr, 2015). This was a connective action that united users in a spontaneous and personalized way, giving shape to the news environment that characterizes this social network (Bruns & Burgess, 2012). The meeting formed community structures for Spanish-speaking and English-speaking users (Conover & al., 2011). These were reinforced by a testable experience of uses and gratifications, an approach employed by some researchers to understand how people use certain media to satisfy needs (Katz, Blumler, & Gurevitch, 1973; Jungherr, 2015; Chen, 2011; Parmelee & Bichard, 2011; Liu & al., 2010).

Beyond the uses, this article analyzes digital media practices (Couldry, 2012) and cultural resources, through which users gave meaning to their participation in this space of the public sphere. Twitter users joined the conversation through emotional frameworks, understood as the set of emotional filters socially constructed for the individual to understand and interpret the world (Goffman & Rodríguez, 2006).

For Mexicans, Trump’s visit was framed emotionally in a complex bilateral relationship between neighbors who, although sharing a border of 1,984 miles (3,193 kilometers) (International Boundary and Water Commission, 2018), have been defined as distant because of their economic and cultural differences (Riding, 2011). Between 1965 and 2015, 16.2 million immigrants left from Mexico to the United States (Krogstad, 2016). In the last 25 years, the United States has tightened immigration policy on its southern border to curb illegal immigration, reinforcing it in 2006 with the issuance of the Secure Fence Act. Even before Trump’s arrival in politics, differences over immigration had been settled through diplomatic channels.

1.2. Twitter as an extension of the political communication space

Twitter has been studied for its role in disseminating news and in constructing a transnational public agenda (Bruns & Burgess, 2012; Hermida, 2010). It has also been studied for its communicative possibilities as a facilitator in organizing multitudes (Bennett & Segerberg, 2013); for its conversational logic and for the affective load that is transferred from the individuality of the private sphere to the public sphere in flexible mobility, characteristic of citizen practice in time of networks (Ranie & Wellman, 2012; Hansen, 2011; Papacharissi, 2015). It has been studied as a mediator of reality, which offers the opportunity to know what is being said about politics (Jungherr, 2015).

Its public carries out news coverage (Hermida, 2010; Lotan & al., 2011) or monitoring of issues of interest (Deuze, 2008), a phenomenon analyzed by various researchers, who agree that Twitter more closely resembles informative media than social network (Kwak & al., 2010; Hermida, 2010; González-Bailón & al., 2011); a characteristic that makes it useful for mobilization and activism.

Twitter facilitates connections and sharing symbolic resources to the entire hybrid media environment that, according to Chadwick (2013), is made up of different platforms and actors with different levels of relationships, who post, share, negotiate meanings and select information in a continuous work of curating content permeated by diverse emotions.

The conversations are structured through different semantic conventions. Hashtags serve to organize the issues –of unease or support– and to stimulate participation in which people negotiate the meanings of actions (Jungherr, 2015). They allow the user to get in touch with audiences beyond their timeline –made up of those they follow– and makes the search for topics functional. According to Bruns and Burgess (2012), the discursive communities around the hashtags allow Twitter to be recognized as a network for dissemination and discussion of news topics.

Retweets contribute to the conversational ecology by replicating a user’s information and mixing it with opinion and testimony of participation, without necessarily agreeing (Boyd, Golder, & Lotan, 2010; Honey & Herring, 2009; Cha & al., 2010; Papacharissi & Oliveira, 2012). Mentions are another convention, considered a measure of influence –together with retweets and number of followers– that favor the viralization that characterizes Twitter.

Some studies consider structural factors of participating in the social network, such as connectivity. Mexico is an emerging economy, with broad swaths of its population not connected: 57.4% of Mexicans have Internet access (INEGI, 2016) and there are barely 9 million Twitter users in Mexico (Brandwatch, 2016). In the United States, 88.5% of the population has access to the Internet (Internet Live Stats, 2016), and there are 56.8 million Twitter users there (Statista, 2016).

2. Materials and methods

Using Twitter’s API, 352,203 tweets were captured between August 30 and September 2 through the following hashtags: #EPN, #Trump, #QuePeñaTrump #TrumpalmuseoMyT #TrumpInMexico, #Trumpen Mexico, #TrumpenMéxico #SrTrumpcontodorespeto and #Trumpnoeresbienvenido. The integration of a corpus by these semantic conventions leaves out part of the conversations, although it is an accessible way to capture unstructured data. The following communicative conventions were taken as categories of analysis: hashtags, mentions, retweets and the content of the conversation.

A mixed methodology focus was adopted. Data mining allowed the possibility of analyzing patterns of conversation frequency and intensity; this is a technique that helps to extract value from an unstructured database. The R program allowed the possibility of analyzing different semantic conventions in the tweets, actors and cultural resources quickly, which then, from sub-samples, were observed to analyze in a more focused way the profiles of intensive and influential actors and their digital media practices. The analysis of social networks, according to which a social environment is expressed in patterns and tendencies in agreement with the interrelation of actors, permitted the analysis of the structures configured in the conversations (Wasserman & Faust, 1994). To recognize the structures of the networks of influential actors, the program Gephi 8.2 was used.

To analyze the emotional framework, content analysis was chosen, a technique that allows for trustworthy and replicable inferences from texts in their context, to understand qualitative variables such as the emotions behind the tweets (Krippendorff, 1997). For the analysis, a sub-sample of 10,000 tweets was chosen randomly and divided into two languages (5,000 in English and 5,000 in Spanish).

3. Results

The visit was the object of multilingual conversations, with English and Spanish dominant without being able to determine territorial location, since not all users activate geolocation. From the sample of tweets analyzed using hashtags, 46% were written in Spanish and 54% in English; other languages such as French, German, and Arabic were identified, which speaks to a transnational conversation.

Connective actions on Twitter are usually detonated by external stimuli, but in this case, it was sparked from within the network, when at 9:33 pm EDT on August 30, 2016, Trump tweeted: “I have accepted the invitation of President Enrique Peña Nieto, of Mexico, and look very much forward to meeting him tomorrow”. Six minutes later, the Mexican presidency confirmed this. The connective action was activated within minutes, generating two tweets per minute until midnight. No traditional Mexican media published the scoop; the newspaper The Washington Post took care of that.

The most-watched television news in Mexico, TV Azteca, announced the news in the last five minutes of its transmission, while the Televisa network barely mentioned it. Mexican digital newspapers began to report on it around 10:00 pm CDT, taking the tweets as their source. Between 6:00 am and 9:00 am CDT on August 31, in Spanish 45 tweets per minute were registered, versus only 5 in English. The information dynamics for both samples had different behaviors, as can be observed in Figure 1.

The period with the greater color density indicates more tweets for each language; although messages in Spanish began around 8:00 am and lasted until after midnight, the moment of greatest intensity was 3:15 pm, whereas in English the greatest intensity occurred between 4:00 pm and 8:00 pm with more than 20,000 tweets per hour.

The climax of the visit corresponds to the greatest volume of tweets, which was in the afternoon of August 31, after the closed-door meeting when both politicians offered a joint message in which Peña gave a conciliatory speech. This contrasted with the collective imaginaries of Mexicans reflected on Twitter, who expected a confrontation with Trump, who at the beginning of his campaign had referred to Mexicans as criminals (Time, 2015).

During the live message, the Mexican presidency prohibited questions from the press, a common practice in Mexico but not in the United States, so journalists from CNN and ABC, respectively, interrupted to ask whether they had spoken about the border wall. Before Peña’s bewilderment, Trump asserted that they had spoken about the wall, but not about who would pay for it. In that moment, Twitter’s reactive profile was obvious as the flow of messages reached a rhythm of four tweets per second, an intensity that continued for several hours, confirming that the news-style environment in controversial events is articulated in a hybrid way – that is, on social networks and traditional media such as radio and television.

Faced with criticism for not responding to Trump, Peña resorted to Twitter hours later to clarify that he had indeed said that Mexico would not pay for the wall: “At the beginning of the conversation with Donald Trump, I made it clear that Mexico will not pay for the wall”. Twitter became a weapon to settle issues that could have been resolved using diplomacy (Figure 2).

Upon his return to the United States, Trump gave a speech in Arizona reconfirming his conviction that Mexico should pay for the wall (Politico.com, 2016). For the international press, Peña had legitimized Trump’s threats against Mexico. This caused a drop in his popularity since 75% of Mexicans considered the visit to be unfavorable (AFP, 2016).

On September 1, the conversation continued. The Mexican president sent a second tweet: “I repeat what I told you personally, Mr. Trump: Mexico would never pay for a wall. twitter.com/realdonaldtrum..”. He confirmed his tweet from the previous day, which would be refuted by the candidate, who in another tweet reaffirmed that Mexico would pay for the wall. For Republicans, it had been a triumph, while for the Democratic candidate Hillary Clinton the exchange of tweets showed that her opponent lied and had embarrassed the United States (Merica, 2016).

The most used hashtag in English and Spanish was #TrumpenMexico, while the volume was greater in English, 188,964 tweets versus 166,239 tweets in Spanish.

3.1. Intensive actors and digital media practices

Two sub-samples were taken, one of the users in Spanish and the other in English, to be able to identify the actors who used Twitter most intensively during this connective action.

The 20 users who tweeted the most about the issue were selected in each language, even though previous studies have shown that those who publish the most are not the most influential users (Cha & al., 2010; Wu & al., 2011; Bakshy & al., 2011). Their usage patterns were explored, as well as the digital practices through which they became connected to the visit. To analyze the frequency of the activity of the 20 most active users in each language (Table 1) an average was taken of the quantity of total tweets made by their accounts divided by the number of days since the creation of each account. In Spanish, the publications of the 20 most active users increased 80%, reaching an average of 105.9 tweets per day. For the 20 most active in English, activity increased 70%, reaching 121.35 tweets per day. The intensity of use confirms hypotheses regarding the reactive and temporal character of Twitter during controversial events.

Observing the time line of each user, it was detected that these were politically active users, which confirms that opinions on Twitter are not representative of public opinion, but it is a politicized public that reacts to political events, creating a news flow. The number of followers in the sub-sample of intensive users raged from 30 to 54,000 followers, which denotes a great variety of profiles. In comparing them, important differences were found in relation to their involvement: 85% of the most active users’ accounts in Spanish were anonymous, versus 45% of those in English, which coincides with previous studies that show a relationship between anonymity and accounts that emit the most tweets about polemic issues (Peddinti, Ross, & Cappos, 2014).

Of the accounts in Spanish, 100% were adverse to both Peña and Trump and condemned the visit, coinciding with public opinion surveys carried out in Mexico (AFP, 2016). Among the accounts in English, a greater balance was found: 50% supported the Republican and the remaining 50% supported Hillary Clinton, which suggests that the intensity and practices of users were influenced by the presidential campaign.

3.2. Influential actors and communities of influence

Influence has been studied by social and cognitive sciences. The theory of diffusion signals that a minority of users, called influencers, can persuade others (Rogers, 2010) and establishes that when these reach a certain network, a chain reaction can be achieved at low cost. There are other factors that determine influence, such as the interpersonal relationship between users and their disposition (Watts & Dodds, 2007). Although there is no consensus on how to measure influence on Twitter, an analysis was carried out based on two variables:

• Direct influence. This is represented by the quantity of followers a determined user has.

• Retweet (RT) influence. This can be measured by the number of RT a user’s content generated.

There are studies that analyze the influence of mentions, measured by the number of times a determined user is mentioned to involve others in the conversation. In this case, the most-mentioned users were the two political leaders.

3.2.1. Direct influence

We chose the 20 users with the most followers in each language. We observed in both sub-samples that journalists, media, and performers were the influencers based on their number of followers. As can be seen in Table 2, in Spanish, influencers have fewer followers, probably because the number of users is substantially higher in the United States.

This finding reinforces the hypothesis previously explored in the literature, regarding influential personalities on social networks who are directly connected and can have high one-on-one interaction. In this action, few individuals served as nodes and conversational pivots. In the case of the Spanish-language sub-sample, only media, journalists and Mexican television personalities stand out, whereas in the English-language one media such as the French @France24 and the Canadian paper @TorontoStar stand out, but also a Mexican actress (@ADELAREGUERA) and a Mexican journalist residing in Los Angeles (@LeonKrauze), who tweet in English and Spanish. This intertwining of influential actors diffuses national borders and reaffirms Twitter’s transnational character.

3.2.2. Retweet influence

The most retweeted users were not those who have the most followers (Table 3). For users in Spanish, opportunistic actors who inserted themselves into the political conversation for other ends, such as promotion, stand out, as in the case of a museum in Mexico City (@MuseoMyT). This is a characteristic of social networks that deserves further research.

In the network of RT in English, celebrities and a base of Trump supporters stood out, although we also observed Clinton supporters and mass media. Some of the pro-Trump accounts were suspended after the event, which suggests the use of bots, a phenomenon documented by experts who report that one-fifth of the Twitter conversation related to the election in the United States was conducted by bots (Bessi & Ferrara, 2016).

Retweets represent the influence of a particular user beyond a one-on-one interaction, as those messages can reinforce an argument and have broad dissemination. The analysis of social networks combined with observation of the profiles of the actors who generated the most RT permitted a delineation of the communities that formed around them. These communities were formed from those nodes that were more densely connected among themselves than with the rest of the network.

In the English-language sub-sample, it was observed that the conversation was inscribed within the Republican and Democratic battle for the presidency. In the network in Spanish, four large communities were detected, with an actor standing out that was not directly connected to the issue, like the Museum of Memory and Tolerance in Mexico, which found an opportunity to get its message out using hashtags inviting Trump to visit the museum and which was the most retweeted node that articulated various communities around it:

• Mr. Trump: For you it’s free.

• Mr. Trump: Come on to remember that we are all equal.

3.3. Emotional frameworks and cultural contexts

Previous studies such as that of Hong, Convertino y Chi (2011) have found substantial cultural differences in the use of Twitter according to the linguistic community, which was corroborated.

This study does not aim to delve into the relationship between language and national identity. We assume, as Even-Zohar (1999), that language is only one part of the maximal cultural complexity in a world configured by migrations and cultural hybridizations such as those observed in Mexico and the United States.

Twitter is a transnational network in which users declare the language in which they write, so the conversation was divided into two clusters. To understand the meaning that users imparted upon the conversation and the emotional framework behind the connective action, a random sampling of 5,000 messages was taken for each language, and Twitter’s temporal narratives were analyzed using the following categories: taunt, support, rejection, surprise, and informative tweet. “Other” and “insufficient” were incorporated for difficult-to-categorize tweets, and “not related” for those that use the hashtag to talk about other issues.

In the composite in English, the predominant posture was support of Trump. Some 40.60% applauded the episode and 24.22% disliked it (this category also included all the tweets that disparaged the politicians). Some 9.72% of the messages were categorized as taunts. The “Other” category, which had other intentions related to the issue of the visit, represented 10.68% in English and 3.66% in Spanish, as can be seen in Table 4. This category included messages to Clinton, both positive and negative. Some 7.18% were informative messages and live transmissions. In the English-language sample, an involvement permeated by electoral content was observed.

The sample in Spanish got involved with the visit in a different way: more than half of the tweets (59%) were messages of rejection or dislike related to the visit and against the politicians. The Spanish-speakers included taunts in the conversation (21.38%) with 1,069 tweets –versus 486 in English– recorded in this category. Only 30 supportive tweets were found (0.6%). It can be held that the emotional framework was permeated by Trump’s insults against Mexicans.

Some of the messages were accompanied by memes and graphic elements to ridicule the politicians, resources that were not used significantly in English. The use of grandiloquent words was detected as a recurring resource to express rejection, dislike and taunt – emotions that were dominant in this sample. These categories were followed by neutral or informative tweets, with 12% of messages not carrying connotation and that aimed to cite, give information or attach some news. Another phenomenon worthy of study is the messages mixing English and Spanish, of which 34 cases were found.

4. Discussion and conclusions

Massive data analysis offered a map of a phenomenon that occurred frenetically through an infinite number of variables, and the ability to correlate them with each other. With mixed techniques, a map of the conversation was drawn, and thanks to the focalization allowed by digital observation and content analysis the emotional frameworks behind this connective action were understood.

Bilingual content analysis allows the others to maintain that tweeting is a cultural practice in which contexts are intermixed and various worldviews are expressed. Hypotheses related to the affective reactions of the Twitter public before newsworthy events were reinforced, usually reactive based on emotional frameworks and from specific cultural and political contexts.

The cultural and political context is the determining factor for the meaning of the conversations, which requires context analysis for a full understanding of the dynamics of transnational digital communication, in which traditional media continue to play a relevant role. A challenge for future research is to improve using automated learning techniques, the processing of emotions in languages other than English; for this more studies on the role of emotions in contemporary politics are pertinent.

For now, there is a broad understanding generated from the Anglo-Saxon scientific world, but there is a need for studies from other cultural and linguistic contexts, especially from the sociopolitical reality of the so-called Global South. A bot analysis is pertinent to current studies, for which automatization is a necessary variable in political communication.

The analysis in English confirms that the visit was part of the US campaigns in which party machinery was clear in the intensive and influential users supporting Trump and Clinton, respectively. The social network analysis was useful to sustain this finding, since we found two well delineated clusters of followers of each candidate.

Thanks to the mixed methods, it was possible to ratify the influence of sociopolitical context on Twitter conversations. On the one hand, an action framed within the electoral context of the United States, in which immigration was a central issue, and on the other hand, the reactive and spontaneous character of users outraged by Trump’s xenophobic affronts.

In the Spanish-language connective action, the network was formed in a less centralized way. Here, many actors participated with different aims, which could be detected with the closeness allowed by digital observation, manifesting that public opinion on Twitter is not always driven by journalists, outraged people, bots or influential actors, but also by actors who find an opportunity to enter into and create a meta conversation in search of particular objectives, which complicates the study of digital public opinion.

Studies of conversations on the transnational scale that investigate the meeting points between different cultures and political context are necessary to strengthen hypotheses relating to echo chambers as a phenomenon characteristic of digital public opinion. The hypothesis touched upon in the literature about the lack of horizontality in social networks was reinforced; this action was tweeted about more in English, which could be due to the degree of connectivity and participative culture in each country, which leads to inequality in constructing an international agenda from social networks. This analysis can help to pose questions for future research about the risks of practicing politics on Twitter to settle controversies without the mediation of traditional media, as Trump did in his first year as president.


AFP (2016). Visita de Trump a México golpea la popularidad de Peña Nieto. El Economista. (https://goo.gl/DM8yeZ).

Ahmed, A., & Malkin, E. (2016). Mexicans Accuse President of ‘Historic Error’ in Welcoming Donald Trump. The New York Times, A17. (https://goo.gl/yb1Kbu).

Bakshy, E., Hofman, J.M., Mason, W.A., & Watts, D.J. (2011). Everyone’s an influencer: Quantifying influence on twitter. Proceedings of the fourth ACM International Conference on Web Search and Data Mining (pp. 65-74). ACM. https://doi.org/10.1145/1935826.1935845

Bennett, W.L., & Segerberg, A. (2013). The logic of connective action: Digital media and the personalization of contentious politics. Massachusetts: Cambridge University Press. (https://goo.gl/NxxV1Z).

Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US presidential election online discussion. First Monday, 21(11), https://doi.org/10.5210/fm.v21i11.7090

Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. System Sciences (HICSS): 43rd Hawaii International Conference On (pp. 1-10). IEEE. https://doi.ieeecomputersociety.org/10.1109/HICSS.2010.412

Brandwatch. (2016). Estadísticas de TW. Brandwatch. (https://goo.gl/rhs3vv).

Bruns, A., & Burgess, J. (2012). Researching news discussion on Twitter: New methodologies. Journalism Studies, 13(5-6), 801-814.

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P.K. (2010). Measuring user influence in twitter: The million follower fallacy. ICWSM, 10(10-17), 30. https://doi.org/10.1145/2897659.2897663

Chadwick, A. (2013). The hybrid media system: Politics and power. New York: Oxford University Press.

Chen, G.M. (2011). Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Computers in Human Behavior, 27(2), 755-762. https://doi.org/10.1016/j.chb.2010.10.023

Conover, M., Ratkiewicz, J., Francisco, M.R., Gonçalves, B., Menczer, F., & Flammini, A. (2011). Political polarization on Twitter. ICWSM, 133, 89-96. (https://goo.gl/JFkQUD).

Corasaniti, N., & Ahmed, A. (2016). Donald Trump to visit Mexico after more than a year of mocking it. The New York Times, A1. (https://goo.gl/A9c5as).

Couldry, N. (2012). Media, society, world: Social theory and digital media practice. Cambridge: Polity. (https://goo.gl/dMkYYp).

Deuze, M. (2008). The changing context of news work: Liquid journalism for a monitorial citizenry. International Journal of Communication, 2, 18. (https://goo.gl/xR3Q7j).

Donald J. Trump [realDonaldTrump] (2016-08-30). I have accepted the invitation of President Enrique Pena Nieto, of Mexico, and look very much forward to meeting him tomorrow. [Tweet]. (https://goo.gl/o3ieL6).

Donald J. Trump [realDonaldTrump] (2016-09-01). Mexico will pay for the wall! [Tweet]. (https://goo.gl/KDfJ1i).

Enrique Peña-Nieto [EPN] (2016,1 de septiembre). Repito lo que le dije personalmente, Sr. Trump: México jamás pagaría por un muro. [Tweet]. (https://goo.gl/vdYSa1).

Enrique Peña-Nieto [EPN] (2016-08-30). Al inicio de la conversación con Donald Trump dejé claro que México no pagará por el muro. [Tweet]. (https://goo.gl/2qsQNX).

Even-Zohar, I. (1999). La posición de la literatura traducida en el polisistema literario. Teori´a de los Polisistemas, 1, 223-231. (https://goo.gl/E7cbxE).

Goffman, E., & Rodríguez, J.L. (2006). Frame analysis: Los marcos de la experiencia. Madrid: Centro de Investigaciones Sociológicas.

González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific Reports, 1, 197. https://doi.org/10.1038/srep00197

Hansen, L.K., Arvidsson, A., Nielsen, F.Å., Colleoni, E., & Etter, M. (2011). Good friends, bad news-affect and virality in Twitter. In Park J.J., Yang L.T., & Lee C. (Eds.), Future information technology. Communications in computer and information science future information technology (pp. 34-43). Berlin: Springer. https://doi.org/10.1007/978-3-642-22309-9_5

Hermida, A. (2010). Twittering the news: The emergence of ambient journalism. Journalism Practice, 4(3), 297-308. https://doi.org/10.1080/17512781003640703

Honey, C., & Herring, S.C. (2009). Beyond microblogging: Conversation and collaboration via Twitter. System Sciences, 2009. HICSS’09: 42nd Hawaii International Conference On (pp. 1-10). IEEE. https://doi.org/10.1109/hicss.2009.89

Hong, L., Convertino, G., & Chi, E.H. (2011). Language matters in Twitter: A large scale study. ICWSM. (https://goo.gl/7yN5Ku).

INEGI (Ed.) (2016). Estadísticas a propósito del día Mundial de Internet. (https://goo.gl/ed9Aet).

Internet Live Stats (Ed.) (2016). Internet live stats 2016. (https://goo.gl/3UQcZL).

Jungherr, A. (2015). Analyzing political communication with digital trace data. Switzerland: Springer. (https://goo.gl/8CWHZs).

Katz, E., Blumler, J.G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509-523. https://doi.org/10.1086/268109

Krippendorff, K. (1997). Metodología de análisis de contenido. Teoría y práctica. Barcelona: Paidós.

Krogstad, J. (2016). 5 facts about Mexico and immigration to the U.S. Pêw Research. (https://goo.gl/eNZn3N).

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceedings of the 19th international Conference on World Wide Web (pp. 591-600). Nueva York: ACM. https://doi.org/10.1145/1772690.1772751

Liu, I.L., Cheung, C.M., & Lee, M.K. (2010). Understanding Twitter usage: What drive people continue to Tweet. In PACIS, 92, 928-939. (https://goo.gl/fq2iZh).

Lotan, G., Graeff, E., Ananny, M., Gaffney, D., & Pearce, I. (2011). The Arab Spring, the revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communication, 5(31). (https://goo.gl/bX6Ebo).

Memoria y Tolerancia [MuseoMyT] (ed.) (2016-08-31). #SrTrumpConTodoRespeto, lo invitamos a que nos visite para recordar la historia y no la repita. #TrumpAlMuseoMyT [Tweet]. (https://goo.gl/oVMDT2).

Memoria y Tolerancia [MuseoMyT] (Ed.) (2016-08-31). #SrTrumpConTodoRespeto, lo invitamos a que nos visite para recordar la historia y no la repita. #TrumpAlMuseoMyT [Tweet]. (https://goo.gl/VJ3xB3).

Merica, D. (2016). Clinton: In Mexico ‘Trump just failed his first foreign test’. CNN. (https://goo.gl/fksyKB).

Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. New York, NY: Oxford University Press.

Papacharissi, Z., & Oliveira, M. (2012). Affective news and networked publics: The rhythms of news storytelling on# Egypt. Journal of Communication, 62(2), 266-282. https://doi.org/0.1111/j.1460-2466.2012.01630.x

Parmelee, J.H., & Bichard, S.L. (2011). Politics and the Twitter revolution: How tweets influence the relationship between political leaders and the public. London: Lexington Books. (https://goo.gl/pmRtWH).

Peddinti, S.T., Ross, K.W., & Cappos, J. (2014). On the Internet, nobody knows you’re a dog: A Twitter case study of anonymity in social networks. Proceedings of the Second ACM Conference on Online Social Networks (pp. 83-94). Nueva York: ACM. (https://goo.gl/pAgVTL).

Politico (Ed.) (2016). Full text: Donald Trump immigration speech in Arizona. Político. (https://goo.gl/hLVjLK).

Presidencia México [PresidenciaMX] (2016-08-30). El Señor @realDonaldTrump ha aceptado esta invitación y se reunirá mañana en privado con el Presidente @EPN. [Tweet]. (https://goo.gl/zFuPSF).

Riding, A. (2011). Distant neighbors: A portrait of the Mexicans. New York: Vintage Books. (https://goo.gl/WBMfJK).

Rogers, E.M. (2010). Diffusion of innovations. New York: Free Press. (https://goo.gl/tftE3J).

Secretaría de Relaciones Exteriores (Ed.) (2016). Comercio México-Estados Unidos. (https://goo.gl/Qd3gBb).

Secure Fence Act (2006). Public Law 109-367 109th Congress. Congressional Record, US, 26th october 2006. (https://goo.gl/HjdUv8).

Statista (2016). Number of twitter users in the United States from 2014 to 2020 (in millions). Statista. (https://goo.gl/VVdzcg).

The Economist (Ed.) (2016). The unspeakable and the inexplicable. The Economist. (https://goo.gl/cMERzb).

Time (Ed.) (2015). Here’s Donald Trump’s Presidential announcement speech. Time. (https://goo.gl/2CRgBy).

Washington Post [WashingtonPost] (Ed.) (2016-08-30). Breaking: Trump considers meeting in Mexico with the country’s president ahead of wednesday immigration speech. [Tweet]. (https://goo.gl/7WTsZ6).

Wasserman, S., & Faust, K. (1994). Social network analysis. Methods and applications. Cambridge University Press. (https://goo.gl/q2KgCM).

Watts, D.J., & Dodds, P.S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-458. https://doi.org/10.1086/518527

Wu, S., Hofman, J.M., Mason, W.A., & Watts, D.J. (2011). Who says what to whom on Twitter. Proceedings of the 20th International Conference on World Wide Web (pp. 705-714). Nueva York: ACM. https://doi.org/10.1145/1963405.1963504