GATE's submission wins 2nd place in United Nations General Assembly Resolutions Extraction and Elicitation Global Challenge

In May 2019 we submitted a prototype to the United Nations General Assembly Resolutions Extraction and Elicitation Global Challenge, which asked for submissions using mature natural language processing techniques to produce semantically enhanced, machine-readable documents from PDFs of UN GA resolutions, with particular interest in identifying named entities and items in certain thesauri and ontologies and in making use of the document structure (in particular, the distinction between preamble and operative paragraphs).

Our prototype included a customized GATE application designed to read PDFs of United Nations General Assembly resolutions and identify named entities, resolution adoption information (resolution number and adoption date), preamble sections, operative sections, and references to keywords and phrases in the English parts of the UN Bibliographical Information System thesaurus.

We downloaded and automatically annotated over 2800 resolution documents and pushed the results into a Mímir index to allow semantic search using combinations of the entities and sections identified, such as the following (more examples are provided in the documentation that we submitted):

• find sentences in operative paragraphs containing a person and an UNBIS term;
• find preamble paragraphs containing a person, an organization, and a date;
• find combinations referring to a specific UNBIS term.

We also developed an easier to use web front end for exploring co-occurrences of keywords and semantic annotations.

We are excited to receive the second place award, along with an invitation to improve our work with more feedback and a "lessons learned" discussion with the panel. The panel highlighted in particular the submission of comprehensive and testable code, and the use of GATE as a mature respected framework.

Our GitHub site contains the information extraction and search front end software, licensed under the GPL-3.0 and available for anyone to download and use.

Toxic Online Discussions during the UK European Parliament Election Campaign

The Brexit Party attracted the most engagement on Twitter in the run-up to the UK European Parliament election on May 23rd, their candidates receiving as many tweets as all the other parties combined. Brexit Party leader Nigel Farage was the most interacted-with UK candidate on Twitter, with over twice as many replies as the next most replied-to candidate, Andrew Adonis of the Labour Party.

We studied all tweets sent to or from (or retweets of or by) UK European Election candidates in the month of May, and classified them as abusive or not using the classifier presented here. It must be noted, in particular, that the classifier only identifies reliably whether a reply is abusive or not. It is not sufficiently accurate for us to reliably judge the target politician or party of this abusive reply. What this means is that we can only reliably identify which EP candidates triggered abuse-containing discussion threads on Twitter, but that often this abuse is actually aimed at other politicians or parties.

In addition to attracting the most replies, the Brexit Party candidates also triggered an unusually high level of abuse-containing Twitter discussions. In particular, we found that posts by Farage triggered almost six times as many abuse-containing Twitter threads than the next most replied to candidate, Gavin Esler of Change UK, during May 2019.

There is an important difference, however, in that that many of the abuse-containing replies to posts by Farage and the Brexit Party were actually abusive towards other politicians (most notably the prime minister and the leader of the Labour party) and not Farage himself. In contrast, abusive replies to Gavin Esler were primarily aimed at the politician himself, triggered by his use of the phrase "village idiot" in connection with the Leave Campaign.

Candidates from other parties that triggered unusually high levels of abuse-containing discussions were those from the UK Independence Party, now considered far right, and Change UK, a newly formed but unstable remain party. Change UK was the most active on Twitter, with candidates sending more tweets than other parties. Gavin Esler was the most replied-to Change UK candidate, and also received an unusually high level of abuse. The abuse often referred to his use of the phrase "village idiot" in connection with the leave campaign, which resulted in anger and resentment.

In contrast, MEP candidates from the Conservative and Labour Parties were not hubs of polarised, abuse-containing discussions on Twitter.

What these findings, unsurprisingly, demonstrate is that politicians and parties who themselves use divisive and abusive language, for example, to brand political opponents as “village idiots”, “traitors”, or as “desperate to betray”, are thus triggering the toxic online responses and deep political antagonism that we have witnessed.

After the Brexit Party, the next most replied-to MEP candidates were from the Labour partyAfter the Brexit Party, the next most replied-to party was Labour, according to the study, followed by Change UK.

MEP candidates from both the Liberal Democrats and the Green Party were also active on Twitter, with the Green MEP candidates second only to Change UK ones for number of tweets sent, but didn't get a lot of engagement in return. The Liberal Democrats in particular received a low number of replies. This may suggest that these parties became the choices of default for a population of discouraged remainers, as both made gains in the election. Both parties attracted a particularly civil tone of reply.

Brexit Party candidates were also the ones that replied most to those who tweeted them, rather than authoring original tweets or retweeting other tweets.

Acknowledgements: Research carried out by Genevieve Gorrell, Mehmet Bakir, and Kalina Bontcheva. This work was partially supported by the European Union under grant agreements No. 654024 SoBigData and No. 825297 WeVerify.