Monday, 12 August 2019

In the News: Online Abuse of Politicians, BBC


We've been working together with the BBC to bring public attention to the issue of online abuse against politicians. Rising tensions in Q1 and Q2 of 2019 meant that politicians were seeing more verbal abuse on Twitter than we have previously observed. The findings were presented on the 6 o'clock and 10 o'clock news on Tuesday, August 6th, and you can see in the histogram above that we found the level of incivility rising to almost 4%. You can see the BBC article describing the work here.

The BBC also did a survey. They found 139 MPs out of the 172 who responded to their survey who said either they or their staff had faced abuse in the past year. More than 60% (108) of those who replied said they had been in contact with the police about threats in the last 12 months.

We found that levels of abuse on Twitter fluctuate over time, with spikes driven by events such as the death of IS bride Shamima Begum's baby or key events in the Brexit negotiations. Labour MP David Lammy has received the most abuse of any MP on Twitter so far this year.

As previously, we also found that on average, male MPs attract significantly more general incivility than female ones, though women attract more sexist abuse. Conservative MPs on average, as previously, attracted significantly more abuse than Labour ones, perhaps because they are in power. Sexist abuse is the most prevalent, as compared with homophobia or racism.

Tuesday, 30 July 2019

GATE Cloud services for Google Sheets featured in the CLARIN Newsflash

CLARIN ERIC is a research infrastructure through Europe and beyond to encourage the sharing and sustainability of language data and tools for research in the humanities and social sciences.  We are pleased to announce that our functions for text analysis in Google Sheets were featured in the July 2019 issue of the CLARIN Newsflash.

We are still working on getting Google to publish our add-on, which we hope to have available in the marketplace in a few months. Until then, you can follow the instructions in our previous blog post to use this tool, which currently provides standard and Twitter-oriented named entity recognition for English, French, and German; named entity linking for English, French, and German; and rumour veracity evaluation for English. In the future we will expand the range of functions to cover a wider variety of GATE Cloud services.

Monday, 15 July 2019

GATE Cloud services for Google Sheets

Spreadsheets are an increasingly popular way of storing all kinds of information, including text, and giving it some informal structure, and systems like Google Sheets are especially popular for collaborative work and sharing data.

In response to the demand for standard natural language processing (NLP) tasks in spreadsheets, we have developed a Google Sheets add-on that provides functions to carry out the following tasks on text cells using GATE Cloud services:
  • named entity recognition (NER) for standard text (e.g. news) in English, French, or German;
  • NER tuned for tweets in English, French, or German;
  • named entity linking using our YODIE service in English, French, or German;
  • veracity reporting for rumours in tweets.

We have demonstrated this work several times, most recently at the IAMCR conference "Communication, Technology and Human Dignity: Disputed Rights, Contested Truths", which took place on 7–11 July at the Universidad Complutense de Madrid in Spain. There we used it to show how organisations monitoring the safety of journalists could automatically add information about entities and events to their spreadsheets. Potential users have said it looks very useful and they would like access to it as soon as possible.

Google sheet showing Named Entity and Linking applications run over descriptions of journalist killings from the Committee to Protect Journalists (CPJ) databases

We are applying to have this add-on published in the G Suite Marketplace, but the process is very slow, so we are making the software available now as a read-only Google Drive document that anyone can copy and re-use. 

The document contains several examples and instructions are available from the Add-onsGATE Text Analysis menu item. The language processing is actually done on our servers; the spreadsheet functions send the text to GATE Cloud using the REST API and reformat the output into a human-readable form, so they require a network connection and are subject to rate-limiting. You can use the functions without setting up a GATE Cloud account, but if you create one and authenticate while using this add-on, rate-limiting will be reduced.



Open this Google spreadsheet, then use FileMake a copy to save a copy to your own Google Drive (you can’t edit the original). For the functions to work, you will have to grant permission for the scripts to send data to and from GATE Cloud services and to use your user-level cache.

This work has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No 687847 (COMRADES) and No 654024 (SoBigData).


Friday, 12 July 2019

Using GATE to drive robots at Headstart 2019


In collaboration with Headstart (a charitable trust that provides hands-on science, engineering and maths taster courses), the Department of Computer Science has just run its fourth annual summer school for maths and science A-level students. This residential course ran from 8 to 12 July 2019 and included practical work in computer programming, Lego robots, and project development as well as tours of the campus and talks about the industry.

For the third year in a row, we have included a section on natural language processing using GATE Developer and a special GATE plugin (which uses the ShefRobot library available from GitHub) that allows JAPE rules to operate the Lego robots.  As before, we provided the students with a starter GATE application (essentially the same as in last year's course) containing just enough gazetteer entries, JAPE, and sample code to let them tweet variations like "turn left" and "take a left" to make the robot do just that.  We also use the GATE Cloud Twitter Collector, which we have modified to run locally so the students can set it up on a lab computer so it follows their own twitter accounts and processes their tweets through the GATE application, sending commands to the robots when the JAPE rules match.


Based on lessons learned from the previous years, we put more effort into improving the instructions and the Twitter Collector software to help them get it running faster.  This time the first robot started moving under GATE's control less than 40 minutes from the start of the presentation, and the students rapidly progressed with the development of additional rules and then tweeting commands to their robots.



The structure and broader coverage of this year's course meant that the students had more resources available and a more open project assignment, so not all of them chose to use GATE in their projects, but it was much easier  and more streamlined for them to use than in previous years.







This year 42 students (14 female; 28 male) from around the UK attended the Computer Science Headstart Summer School.
Geography of male students

Geography of female students

The handout and slides are publicly available from the GATE website, which also hosts GATE Developer and other software products in the GATE family.  Source code is available from our GitHub site.  

GATE Cloud development is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654024 (the SoBigData project).


Wednesday, 3 July 2019

12th GATE Summer School (17-21 June 2019)


12th GATE Training Course: open-source natural language processing with an emphasis on social media

For over a decade, the GATE team has provided an annual course in using our technology. The course content and track options have changed a bit over the years, but it always includes material to help novices get started with GATE as well as introductory and more advanced use of the JAPE language for matching patterns of document annotations.

The latest course also included machine learning, crowdsourcing, sentiment analysis, and an optional programming module (aimed mainly at Java programmers to help them embed GATE libraries, applications, and resources in web services and other "behind the scenes" processing).  We have also added examples and new tools in GATE to cover the increasing demand for getting data out of and back into spreadsheets, and updated our work on social media analysis, another growing field.
Information in "feral databases" (spreadsheets)
We also disseminated work from several current research projects.
Semantics in scientometrics

  • From KNOWMAK and RISIS, we presented our work on using semantic technologies in scientometrics, by applying NLP and ontologies to document categorization in order to contribute to a searchable knowledge base that allows users to find aggregate and specific data about scientific publications, patents, and research projects by geography, category, etc.
  • Much of our recent work on social media analysis, including opinion mining and abuse detection and measurement, has been done as part of the SoBigData project.
  • The increasing range of tools for languages other than English links with our participation in the European Language Grid, which is also supported further development of GATE Cloud, our platform for text analytics as a service.
Conditional processing of multilingual documents

Processing German in GATE
The GATE software distributions, documentation, and training materials from our courses can all be downloaded from our website under open licences. Source code is also available from our github page.

Acknowledgements

The course included research funded by the European Union's Horizon 2020 research and innovation programme under grant agreements No. 726992 (KNOWMAK), No. 654024 (SoBigData), No. 824091 (RISIS), and No. 825627 (European Language Grid); by the Free Press Unlimited pilot project "Developing a database for the improved collection and systematisation of information on incidents of violations against journalists"; by EPSRC grant EP/I004327/1; by the British Academy under the  call "The Humanities and Social Sciences: Tackling the UK’s International Challenges"; and by Nesta.

Thursday, 27 June 2019

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.

Thursday, 6 June 2019

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.