Iadh Ounis
Biographic Data
| ID | 1069307 |
|---|---|
| NAME | Iadh Ounis |
| GIVEN NAMES | Iadh |
| FAMILY NAME | Ounis |
| SIGNATURE | OUNIS I |
| AFFILIATIONS | University of Glasgow |
| ORCID | 0000-0003-4701-3223 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 9 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 2 |
We need to go deeper: Measuring Electoral Violence Using Convolutional Neural Networks and Social Media
Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in soci…
Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election
Social media offers scholars new and innovative ways of understanding public opinion, including citizens’ prospective votes in elections and referenda. We classify social media users’ preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users’ vote preferences to train our machine le…
We need to go deeper: Measuring Electoral Violence Using Convolutional Neural Networks and Social Media
Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in soci…
Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election
Social media offers scholars new and innovative ways of understanding public opinion, including citizens’ prospective votes in elections and referenda. We classify social media users’ preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users’ vote preferences to train our machine le…
Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election
Social media offers scholars new and innovative ways of understanding public opinion, including citizens’ prospective votes in elections and referenda. We classify social media users’ preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users’ vote preferences to train our machine le…
We need to go deeper: Measuring Electoral Violence Using Convolutional Neural Networks and Social Media
Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in soci…
Computer Science (2 works) · Political science (2 works) · Social media (2 works) · Artificial Intelligence (1 works) · Classifier (UML (1 works) · Coding (social sciences (1 works) · Conversation (1 works) · Criminology (1 works) · Data mining (1 works) · Electoral Systems and Political Participation (1 works)