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ElisabettaChisso

Presentazione di Prelaurea - Corso di Laurea Triennale in Ingegneria Elettronica e Informatica

Daten & Analysen

Extended Summary of :
«Don’t let me be misunderstood:
comparing intentions and
perceptions in online discussions»
Laureanda:
Elisabetta Chisso
Relatore:
Prof. Alberto Bartoli
02 marzo 2023

Contesto: conversazioni online nella sezione commenti
sotto a post di pagine pubbliche su Facebook
Problema: disallineamento intenzioni-percezioni interlocutori
Conseguenze:
- Inciviltà
- Conflitti
- Odio
Impatto su:
- Salute mentale
- Vita sociale
- Vita professionale
Introduzione: Problema e Rilevanza

Conversazione =
Utente 1 =
Utente 2 =
Thread =
1. Utente 1 commenta un post
2. Utente 2 risponde al commento
3. Utente 1 risponde
Iniziatore
Risponditore
Insieme di messaggi scambiati
relativamente a un argomento
(internet)
Definizioni:

Dataset:
• 22 milioni di conversazioni
• 3 milioni di post
• 800 mila pagine
• 16000 partecipanti
Indagine: metodo

1. Sovrastima
sistematica
Difficoltà nel percepire gli
obiettivi di ricerca con sicurezza
VM risposta
percezione
2. Incertezza
>> VM risposta
intenzione
Risposte e differenze distributive

Analisi linguistica (1)
Elementi linguistici:
Correlazione:
Elementi linguistici – Obiettivi Percepiti/Intesi
Sì: Linguaggio fattuale – Intenzione di condividere fatti
No: Linguaggio fattuale – Percezione di condividere fatti
• Pronomi
• Aggettivi
• Numeri cardinali
• Modali e avverbi
• Linguaggio soggettivo
• Linguaggio fattuale
• Domande
• …

Analisi linguistica (2)
EI = Etichette Intenzione
EP = Etichette Percezione
Obiettivo Previsione
Percezioni (EP)
Previsione
Intenzioni (EI)
Previsione
Intenzioni (EP)
Condividere Fatti 0,64 0,68 0,65
Ricercare Fatti 0,81 0,82 0,80
Condividere Opinioni 0,72 0,75 0,75
Ricercare Opinioni 0,64 0,72 0,68
AUC : indice delle prestazioni del classificatore
Prevedere intenzioni e percezioni dal testo attraverso un
classificatore addestrato con etichette linguistiche.

Analisi linguistica (3)
Notazione:
PI = Previsione Intenzioni
AUCE (PI) = valore di AUC indice delle prestazioni del
classificatore su PI tramite etichette E.
AUCEI (PI) > AUCEP (PI)
etichette per
prevedere
intenzione
etichette per
prevedere
percezione
≠
Stesse etichette per prevedere intenzioni e percezioni?

Risultati significativi:
Inciviltà thread – Intenzione condivisione fatti
Inciviltà thread – Percezione condivisione opinioni
Correlazione con gli esiti

Conclusioni:
Opportunità:
promuovere interazioni più sane su piattaforme
di discussione online
attraverso la creazione di classificatori che
prevedono intenzioni e percezioni
segnalare agli utenti se possono essere
fraintesi
suggerire strategie concrete
(es: «Invece di x scrivi y»)

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Presentazione_ElisabettaChisso.pdf

1. Extended Summary of : «Don’t let me be misunderstood: comparing intentions and perceptions in online discussions» Laureanda: Elisabetta Chisso Relatore: Prof. Alberto Bartoli 02 marzo 2023

2. Contesto: conversazioni online nella sezione commenti sotto a post di pagine pubbliche su Facebook Problema: disallineamento intenzioni-percezioni interlocutori Conseguenze: - Inciviltà - Conflitti - Odio Impatto su: - Salute mentale - Vita sociale - Vita professionale Introduzione: Problema e Rilevanza

3. Conversazione = Utente 1 = Utente 2 = Thread = 1. Utente 1 commenta un post 2. Utente 2 risponde al commento 3. Utente 1 risponde Iniziatore Risponditore Insieme di messaggi scambiati relativamente a un argomento (internet) Definizioni:

4. Dataset: • 22 milioni di conversazioni • 3 milioni di post • 800 mila pagine • 16000 partecipanti Indagine: metodo

5. 1. Sovrastima sistematica Difficoltà nel percepire gli obiettivi di ricerca con sicurezza VM risposta percezione 2. Incertezza >> VM risposta intenzione Risposte e differenze distributive

6. Analisi linguistica (1) Elementi linguistici: Correlazione: Elementi linguistici – Obiettivi Percepiti/Intesi Sì: Linguaggio fattuale – Intenzione di condividere fatti No: Linguaggio fattuale – Percezione di condividere fatti • Pronomi • Aggettivi • Numeri cardinali • Modali e avverbi • Linguaggio soggettivo • Linguaggio fattuale • Domande • …

7. Analisi linguistica (2) EI = Etichette Intenzione EP = Etichette Percezione Obiettivo Previsione Percezioni (EP) Previsione Intenzioni (EI) Previsione Intenzioni (EP) Condividere Fatti 0,64 0,68 0,65 Ricercare Fatti 0,81 0,82 0,80 Condividere Opinioni 0,72 0,75 0,75 Ricercare Opinioni 0,64 0,72 0,68 AUC : indice delle prestazioni del classificatore Prevedere intenzioni e percezioni dal testo attraverso un classificatore addestrato con etichette linguistiche.

8. Analisi linguistica (3) Notazione: PI = Previsione Intenzioni AUCE (PI) = valore di AUC indice delle prestazioni del classificatore su PI tramite etichette E. AUCEI (PI) > AUCEP (PI) etichette per prevedere intenzione etichette per prevedere percezione ≠ Stesse etichette per prevedere intenzioni e percezioni?

9. Risultati significativi: Inciviltà thread – Intenzione condivisione fatti Inciviltà thread – Percezione condivisione opinioni Correlazione con gli esiti

10. Conclusioni: Opportunità: promuovere interazioni più sane su piattaforme di discussione online attraverso la creazione di classificatori che prevedono intenzioni e percezioni segnalare agli utenti se possono essere fraintesi suggerire strategie concrete (es: «Invece di x scrivi y»)