Author Archives: manolofarci

Pratiche di video sharing su Twitter

Durante il convegno,“Così vicini, così lontani. La via italiana ai social network, Elisabetta Locatelli, Maria Francesca Murru, Simone Carlo, Nicoletta Vittadini (Università Cattolica del Sacro Cuore – Milano) hanno presentato un lavoro dal titolo Pratiche di video sharing su Twitter

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Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e YouTube come sorgente dati

Durante il convegno,“Così vicini, così lontani. La via italiana ai social network“, Davide Bennato (Università di Catania), Fabio Giglietto (Università degli studi di Urbino Carlo Bo) e Luca Rossi (IT University of Copenhagen), hanno presentato un lavoro dal titolo Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e YouTube come sorgente dati

Verso una metodologia dell’analisi visuale su Twitter. Il caso del terremoto in Emilia Romagna

Durante il convegno,“Così vicini, così lontani. La via italiana ai social network“, Laura Gemini (Università degli studi di Urbino Carlo Bo) ha presentato un lavoro realizzato assieme a Giovanni Boccia Artieri, Manolo Farci e Elisabetta Zurovac  (Università degli studi Urbino Carlo Bo) dal titolo Verso una metodologia dell’analisi visuale su Twitter. Il caso del terremoto in Emilia Romagna

 

L’evento catastrofico si rivela un importante quanto interessante luogo di osservazione delle pratiche comunicative e delle strategie narrative messe in moto dai media.

Se i media tradizionali attivano strategie sufficientemente note di rappresentazione della catastrofe – sia nella forma dell’intrattenimento sia in funzione dell’informazione mediante l’utilizzo di precisi criteri selettivi (Luhmann 2000, Chéroux 2010) – i media sociali vanno considerati come ulteriori, se non nuovi, contesti di produzione e circolazione (Jenkins 2013) dell’immaginario catastrofico. Ambienti utili a gestire il trauma in maniera diretta e “di prima mano” da parte degli utenti (Huges e Palen 2009, Bruns e Burgess 2012, Sutton 2010, 2011, 2013, Robinson 2009, Liu 2009, in Italia ad esempio la ricerca di Micalizzi e Farinosi 2013, Gavrila (a cura di 2012), Ragone (a cura di 2012), ecc.).

Certo è che in tutti questi casi l’apporto della comunicazione visuale – cioè la produzione, diffusione, uso di immagini – si rivela centrale per il trattamento simbolico dei grandi determinismi naturali (Durkheim).

Su questi presupposti, il paper presenta i risultati di una ricerca effettuata su 4257 immagini caricate dagli utenti di Twitter il primo giorno del terremoto in Emilia Romagna (20 maggio 2012).

Sulla base di tale analisi, viene proposta una tipologia di immagini a sostegno di una prima e generale ipotesi del lavoro secondo cui in occasione di eventi catastrofici, l’immagine non ha solo una funzione di re-fero cioè di testimonianza del trauma ambientale, né esclusivamente di re-ligo, ossia di  condivisione sociale del dramma. Le immagini condivise sui social network rispondono, piuttosto, all’esigenza di rielaborare in termini simbolici il trauma catastrofico, facendone un’occasione efficace per attivare nuovi rituali di socializzazione e di condivisione collettiva.

L’indagine inoltre si concentra sulla specificità di Twitter – come medium principalmente testuale e caratterizzato da precise dinamiche di connessione fra gli utenti – per individuare, attraverso una prima applicazione della social network analysis, il peso della circolazione delle immagini-tipo fino a mettere in evidenza le immagini-icona e il tipo di rete sociale che ne motiva e spiega l’emergenza e la circolazione.

La convergenza di civic e digital literacy nella partecipazione politica giovanile: riflessioni teoriche e metodologiche

Durante il convegno “Così vicini, così lontani. La via italiana ai social network“, Giovanna Mascheroni (Università Cattolica del Sacro Cuore) ha presentato un lavoro realizzato assieme a Maria Francesca Murru (Università Cattolica del Sacro Cuore) dal titolo La convergenza di civic e digital literacy nella partecipazione politica giovanile: riflessioni teoriche e metodologiche

Lo scenario italiano della Social Tv : tra comportamenti degli utenti e broadcaster

Durante il convegno  “Così vicini, così lontani. La via italiana ai social network“,  Vincenzo Cosenza (BlogMeter) ha presentato un lavoro dal titolo Lo scenario italiano della social tv: tra comportamenti di utenti e broadcaster.

Commentare le trasmissioni televisive e interagire con i broadcaster attraverso i social media sta diventando un’attività sempre più usuale. Ecco perché risulta fondamentale per gli operatori del settore affiancare alle metriche tradizionali, nuovi misuratori di efficacia. La presentazioni si soffermerà su un’analisi estesa, da gennaio ad agosto 2013, delle performance di reti e show su Facebook e Twitter al fine di contribuire alla riflessione sui cambiamenti in atto e sulle nuove metriche della Social TV.

Social Sensing: un’applicazione web basata su Twitter per il monitoraggio di eventi sismici in Italia

Durante il convegno “Così vicini, così lontani. La via italiana ai social network“,  Maurizio Tosoni (Istituto di Informatica e Telematica CNR Pisa) ha presentato un lavoro dal titolo Social Sensing: un’applicazione web basata su Twitter per il monitoraggio di eventi sismici in Italia

La condivisione di informazioni, contenuti e opinioni in rete è ormai una realtà quotidiana e consolidata. L’utilizzo dei Social Media (SM), ovvero di tutti i siti web che consentono la creazione e lo scambio di contenuti generati dagli utenti, è un fenomeno crescente anche in
Italia. Il Social Sensing si basa sulla constatazione che gli utenti, singolarmente o organizzati in gruppi, condividano una quantità tale di informazioni in rete, da fornire una adeguata conoscenza sui temi più disparati.
Le persone possono dunque fungere da “sensori sociali”: è cioè possibile risalire, dall’analisi dei contenuti scambiati, alla rilevazione di eventi che destano allarme sociale come, ad esempio, terremoti, alluvioni o altre situazioni di emergenza.
In questo ambito è fondamentale la capacità di individuare tempestivamente l’evento e riuscire ad avvisare efficacemente gli utenti. Nell’intervento sarà presentata una piattaforma per il monitoraggio, l’analisi e la visualizzazione in tempo reale di eventi che utilizza dati provenienti dai SM.
La mole di dati coinvolti, la moltiplicazione dei soggetti da analizzare e delle piattaforme utilizzate impongono la costruzione di nuove metodologie per la raccolta, l’archiviazione e l’elaborazione diquesti dati. Per caratterizzazione meglio gli eventi sono prese in considerazione anche le informazioni sulla geolocalizzazione e la semantica dei messaggi raccolti. A conclusione verrà mostrata una demo di un’applicazione che consente di rilevare i terremoti attraverso Twitter basata su un’analisi temporale e spaziale in realtime dei tweet raccolti e saranno forniti i risultati di accuratezza del sistema per eventi avvenuti negli utlimi 3 mesi.

Qui le slide dell’intervento

Privacy and SNS: generational differences in managing privacy and disclosure

The balance between privacy and disclosure of personal information is one of the most relevant issues raised by SNS. boyd (2010) highlighted that SNS are characterized by the blurring of public and private. Papacharissi (2010) described SNS as places privately public and publicly private.

The paper “Privacy and SNS: generational differences in managing privacy and disclosure” presented during the conference Social media: Transforming audiences (London 2-3 September 2013) is aimed at discussing some theoretical issues related to privacy and Social network sites and at presenting some preliminary results of the analysis of the interviews carried on in Milan during the research project Online social relations and identity: Italian experience in Social Network Sites.

The management of privacy in SNS is one of the challenge that users have to face. They can use privacy tools provided by the SNSs software, but – according with Altman (1975) – they also have to manage the boundaries of the different network of “friends” through disclosure and hiding of information and protect their “expressive privacy” (Papacharissi 2010).

Privacy – intended as “the claim of individuals, groups or institutions to determine for themselves when, how and to what extent information about them is communicated to others” (Westin 1967) – includes the right to an “inviolate personality” (Johnson 1992) and the right to control the sharing process of one’s contents in order to avoid the “context collapse” described by boyd (2008).

Privacy in SNS then is a question of “audiences” (public or private profiles); “contents” (who can access a specific content, for example in friend’s lists) and “sharing” (who can use my contents). SNS users have to manage complex issues of hiding and disclosure in a communicative space (the social networks) intended to promote sociality and information sharing. They have to re-define the boundaries between public and private contents according to the definition of “what is” the social network communicative space.

Defining communicative spaces and boundaries between public and private is a cultural issue and different scholars highlighted that privacy in SNS is affected by individual’s culture (Dourish & Anderson 2006; Lewis 2008) or by the belonging to different “cultural units”.

Among different “cultural units”, generations can be relevant in understanding differences in “privacy cultures” of SNS users especially regarding the “expressive privacy” (the protection of the process of personal identity building from third party’s interferences – Tufekci 2008).

According with the preliminary analysis of the interviews we can say that some results can be interpreted as the traces of different generational “privacy culture”: values and norms related to privacy management that characterize generational identities.

Vittadini

Alongside these “cultures” privacy emerges as a balance between the benefit and the pleasure to share informations (disclosure) and the control over personal informations (hiding) and the definition of the benefit of SNS use changes according to different “generational cultures”.

According to our preliminary results we can say that for the “early boomers” the benefit is the opportunity to discuss in a public space about personal interest; for the “generation x” is the “edonistic” dimension of building identity tales; for the “generation y” is the benefit of sociality and for the “generation z” is the benefit of self-expression and performativity.

In conclusion we can say that privacy is a “social issue” as it is both managed socially as “once private information is disclosed or others are granted access, the information moves from individual ownership to collective ownership” (Child & Petronio 2011) and related to a shared culture as – for example – the “generational” one.

Nicoletta Vittadini – Università Cattolica del Sacro Cuore

 

Social Media aren’t votes. Primary election and predictive models

Italian version

The case of the 2012 centre-left primary election was the first important italian event of collective participation in a political action that has found in SNSs a valuable sounding board. Thousands of twitters and posts on Facebook have turned the challenge for determine the coalition’s leadership in a great storytelling, where politicians and civil society, thanks to SNSs, have competed on equal terms to give a voice to their demands. Facebook, LinkedIn, Twitter, and other social media tools have changed the way many people communicate, getting the mass political arena denser, more complex and more participatory. This is proved, for instance, by the increasing comments on Twitter during the television debate among the five contenders, that was aired on SkyTg24: according to Blogmeter, the involvement of this primary election has reached record highs in Italy with 127.426 tweets about this subject from 30 minutes before the start of the television program to 30 minutes after the end.

If undoubtedly traditional and social media tools both have central role in shaping political sphere, more controversial is the use of big data extracted from Facebook, Twitter or Google to build a predictive model for the election. In fact, during the campaign for the Republican presidential nomination, many scholars argued about theoretical limits of predictive analysis, that can’t be overcame simply with more advanced technology. Despite a group of researchers of München University in occasion of the German federal election has conducted a content analysis of over 100,000 messages containing a reference to either a political party or a politician and it has proved that Twitter is widely used for the political deliberations, the employ of a statistical algorithms as a model for extracting big data has yet to become an accurate tool able to predict the election outcome (for a literacy review see also this recent article about United States presidential primary).

As Michel Wu explains, we need three requirements to validate any statistical model or algorithm that aims to become predictive – from Apple’s stock price to the percentage shares of the politician: 1. a model or algorithm that compute some predicted outcome (e.g. the leverage of the traffic that is spawned by the politician followers); 2. an independent measure of the outcome that the model is trying to predict (e.g. the opinion poll results) 3. a measure that compares and quantifies how closely the predicted outcome matches the measured independently outcome.

The main point is the second: having a measure that is really independent from the results obtained by the social media monitoring tools. It should be pretty obvious, but the risk is to fall into the fallacy of circular reasoning: that is, using likes and retweets for building a model that is extended to the whole sample. Hence the need to use a variable for comparison: in our case, the opinion polls.

Method

Influenced from the attractive and effective method used by Nate Silver to predict the outcomes ofUnited States presidential primary, we have decided to employ a different prospective to measure the impact of social media on the level of candidates’ popularity. The data extracted from social media are often used as proxies of electoral success of a politician or a party. Sometimes the research is based on quantitive analysis; other times, with the aid of sentiment analysis, it’s more focused on the area of the subjective opinions, emotions and human affects available in the social web in the forms of news, reviews, blogs, chats and even twitters. Our approach is different: we use the data extracted from social media for comparing and correcting the results of exit polls conducted by pollsters and media agencies to query voters about their voting selection. For this purpose, we have picked up 30 exit polls from October 1st to November 23th 2012. The data of exit polls don’t refer to their spreading, but to their effective carrying out. When in a day there are many exit polls, we have taken into account their average value. Data are openly available on the site sondaggipoliticoelettorali.it.

Thanks to collaboration with Blogmeter, we also have picked up – for the most important candidates (Pierluigi Bersani, Matteo Renzi and Nichi Vendola) and for the same period of time – all the main metrics relative to their official presence on Twitter and Facebook  (total number of fun/followers, new fun/followers for a day/, Facebook People Talking About, Twitter Mentions, Facebook and Twitter engagement).

After a set of first analysis focused on the use of all the social media indicators, we have built a predictive model for every candidate based on some correctives that we have computed from Facebook People Talking About and Twitter Mentions. The correctives are estimated on the basis of the temporal correlation between the time trend in consensus measured from the opinion polls  and the trend of the metrics we have taken in account. The model, based on a multiple linear regression (MLR), allows us to estimate the corrective values for obtaining, on the basis of a trend extracted from the polls, a prevision of the electoral result for every candidate.

As we can see from the analysis of the graphs, in the case of Pierluigi Bersani the spread between the data obtained from our prevision and the electoral result is 4.16 percentage points, whereas the spread derived from the data computed by the average of trend time of consensus in the opinion polls, in a period of time from October 1st to November 23th 2012, is 5.41 percentage points. Instead, in the case of Matteo Renzi, the spread between the data obtained from our metrics and the electoral result is 2.85 percentage points, whereas the spread computed by the average of trend time in the same period of time is 3.33 percentage points. At last, if we see the data relative to Nichi Vendola, we notice that our spread is equal to 0.09, whereas the spread between the data of election and the average of polls is 0.12.

We can infer that our model shows an excellent predictive ability – that is apparent mostly in the case of Nichi Vendola – compared with the historical trend of opinion polls in a period of time of about two months. The reason of this predictive ability is because our model is calibrated on a metrics that combine together indicators of engagement obtained both from Facebook and Twitter  and it provides different analysis for every single candidate. Showing data amount separately for every politician of the primary election, we find out, for instance, that the total volume of mention don’t’ always is  positive for the candidate but it assumes a different meaning depending on the politician and, probably, on the material contents. Our data show, for instance, that Vendola’s talking abouts led to a negative coefficient that, most likely,  change electoral preferences.

These research show us how is difficult to build predictive model that not considers the politician’s different communication strategies: in fact, every politician has own discursive style, ways of dialogue and ability to handle many digital platforms. And it should be considered that every candidate has a favored target, with socio-demographic or cultural variables that could be discordant. We should consider not only that the reality of the mass media isn’t statistically representative of the entire population, but also that the specific behaviors of every candidate using the most popular SNS’s  like Facebook and Twitter are to hard to generalize. All of this lead us to rethink the relationship between predictive models and social media, especially when we deal with social events so complex as the politics and the building of a public sphere, that are able to easily overlapping offline and online experiences.

The predictive reality and the social media

In this context, we should consider that SNSs aren’t a representative sample either of the entire population or the voters. It should be a commonplace, but it become a fact not so evident because of the overload of news and researches on every political fact, public debate e.g., that pretend to measure the political engagement rate, starting from the numbers of tweets or comments on politicians’ Facebook pages or those of the political party or the political television shows, e.g.

In fact, the researchers begin to understand that there are specific processes of autoselection that provide a more political powerful use of the net, from all those citizens that are previously involved or that are actives (Norris 2001), or that have the cultural and technological resources needful to practice an active citizenship (Bentivegna 2009). There aren’t a simple process of convergence between voters’ attitude and their offline and online political practices, not useful especially for predictive analysis. Is not the number of the politician’s followers or his level on engagement on a Facebook page that it let us predict the electoral choices. In the case of the 2012 centre-left primary election, Nichi Vendola had the most of fans online, about 250.000 on Twitter and more than 500.000 on Facebook and Matteo Renzi had the most high levels of engagement (see Vincenzo Cosenza’s data analysis), whereas Pierluigi Bersani, the candidate who have gotten the most of voters’ electoral preferences, had a lower amount of data.

This doesn’t mean that there is a clear difference between online and offline spheres. The ongoing incidence of the net in the people’s daily life broadens the political involvement in a continuum that includes the real experience, the media consumption and the practices of online engagement (Dahlgren 2009). But this doesn’t concern all the voters, because not all people actually act in the online life.

Networked public spheres and the interpretation of reality

We must understand how it make possible thinking about this dual form of  representativeness and what kind of relationship exists between these. This question could open the way to remarkable researches in predictive analytics on social networks. The value of likes of political candidates’ pages on Facebook (Giglietto 2012) is connected to politicians’ abilities of using the SNS strategically and having a conversation with users. Nevertheless, this ability can’t necessarily help predicting the election polls, because we must always take into account the weight of the mass media in shaping the public opinion. For this reason our research intends to rethink the use of social media in predictive key, not using this medium as the only source of data, but as a reality that we have to connect with a more traditional source of data as opinion polls.

We need to build a relationship between the different public spheres in which it’s possible to express a public opinion, as it happens in the mainstream media, and a opinion in public, as it happens in the social media. For this reason, we prefer talking about networked public spheres (Boccia Artieri 2012). In that regard, the social media can be considered as conversational correctives of opinion polls. It establishes a connection between the real choice among the politician candidates and the strategic use of SNSs as political communication tools that aim for increasing their visibility and the engagement – see the employ of hashtag for joining people around a issue. It’s a first corrective idea that has met a positive response in our model, that links together the opinion polls with the single candidate’s levels of engagement: Mentions on Twitter and Talking About on Facebook. For improving our model, we should add the sentiment analysis, a method that apply quantitative tools for measuring opinions, feelings and emotions from online textual contents. There are many remarkable examples about this, bur remains unsolved, especially in the political field, the question of irony: how it could treat, e.g., the contents on Twitter and Facebook generated by Marxisti per Tabacci? Moreover, it should also consider that there are other elements that might lead to increase the public sphere, as news websites and blogs, where people generates and shares opinions and tools of engagement (share, like, comments). In that sense, it’s considerable, for instance, that Pierluigi Bersani is the candidate who have obtained the most number of mentions on news websites and blogs. This research on the centre-left primary election in Italy is a first step to an integrated model that aim at reading a complex reality where online and offline must be considered as a continuum. Also in a predictive key.

Research team
Giovanni Boccia Artieri – Università di Urbino Carlo Bo
Manolo Farci – Università di Urbino Carlo Bo
Fabio Giglietto – Università di Urbino Carlo Bo
Luca Rossi – Università di Urbino Carlo Bo
Elisabetta Zurovac – Università di Urbino Carlo Bo

Bibliography

Boccia Artieri G. (2012), Stati di connessione. Pubblici, cittadini, consumatori nella (Social) Network Society, FrancoAngeli, Milano.
Bentivegna S. (2009), Disuguaglianze digitali. Le nuove forme di esclusione nella società dell’informazione, Laterza, Roma-Bari.
Dahlgren P. (2009), Media and Political Engagement. Citizens, Communication and Democracy, Cambridge University Press.
Norris P. (2001), Digital Divide: Civic Engagement, Information Poverty and the Internet Worldwide, Cambridge University Press, Cambridge MA.
Giglietto, F. (2012), If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections. International AAAI Conference on Weblogs and Social Media; Sixth International AAAI Conference on Weblogs and Social Media. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4577

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