=Paper=
{{Paper
|id=Vol-1963/paper495
|storemode=property
|title=FootballWhisper: Transfer Rumour Detection
|pdfUrl=https://ceur-ws.org/Vol-1963/paper495.pdf
|volume=Vol-1963
|authors=Neil Ireson,Fabio Ciravegna
|dblpUrl=https://dblp.org/rec/conf/semweb/IresonC17
}}
==FootballWhisper: Transfer Rumour Detection==
https://ceur-ws.org/Vol-1963/paper495.pdf
FootballWhispers: Transfer Rumour Detection
Neil Ireson1 and Fabio Ciravegna1
Sheffield University, Sheffield, UK
Abstract. Social media has been shown to have potential to predict
various real world events, such as movements in the stock market and
the outcomes of political elections. In this paper we present the Football
Whispers (FW), a website dedicated to fans discussing transfer rumours.
The unique selling point of the site is that it provides a crowdsourced as-
sessment of those rumours, measuring the relative likelihood of a player’s
movements from social media chatter. This talk will focus on the rumour
identification process, highlighting the role of open knowledge graphs and
linked data to augment a domain knowledge-based to enable effective
Named Entity Linking in noisy, informal social media messages.
1 Introduction
Football is the world’s most popular sport, it is responsible for generating the ma-
jority of sporting revenue, in excess of $20 billion per annum, and it is estimated
that over a billion people can be counted as football fans. Social media has be-
come an important tool for maintaining the relationship between fans and their
teams. With Twitter activity assured by both the substantial global fan base and
dedicated localised fans. Football Whispers (FW) (www.footballwhispers.com)
provides a service focused on the rumoured transfer of players between teams.
A fundamental feature of FW is the estimation of the veracity of the rumours;
providing a relative likelihood of a player moving to a given team. The likeli-
hood is determined using the Twitter conversations concerning transfers, this
involves two processes: Named-Entity Linking (NEL); and the assessment and
combination of rumours to determine their relative likelihood.
2 Named Entity Linking & Rumour Detection
The NEL process involves the identification of entities (people and teams) men-
tioned in tweets and the disambiguation of those mentions; linking them to
specific instances in a domain knowledge base (KB). The inherent issues when
dealing with such short text with a high degree of abbreviated, informal lan-
guage use is accentuated by the international, and thus multilingual, conversa-
tions concerning football. The initial domain KB is provided by Opta Sports
(http://www.optasports.com/), while Opta is arguably the de facto source of
football related information it only provides the official and possibly a single
more common name. The 41,238 active players, from 3,266 teams, have only
2
46,631 name variations; with 17,489 (42%) players sharing a last name and 785
(2%) have identical names. In order to increase the number of alternative names
the Opta entities are mapped to Wikidata and DBpedia entities. Wikidata con-
tains 210,375 players and 30,710 teams, although a large number of the players
are inactive, in order to identify potentially ambiguity it is necessary to include
all names which may be mentioned. In addition to players, 11,439 managers,
pundits, referees, etc. are also extracted. The talk will describe the entity map-
ping process, which considers the similarity of entities’ available features, e.g.
string similarity of names and numerical distance of dates, with the importance
of a features being weighted according to the degree of variation in its values.
The mapping process was also applied to DBpedia, which contains 126,790 play-
ers; this resulted in a slight increase in alternative names, primarily due to the
DBpedia extraction of nicknames. In total 32,754 (80%) of Opta players are
mapped, and for these players name variations are tripled to 95,535.
The team and player names are then used to generate a Deterministic Finite
Automata to efficiently extract candidate entity mentions from the message text.
The talk will describe how the contextual disambiguation processes are used
to link mentions to an entity instance, where other entity candidates in the
message provide the context. A name which occurs frequently in small number
of (expected) contexts (e.g. player name mentioned only with their team) is
deemed to maintain its meaning outside those contexts, while a name which
occurs in multiple (unexpected) contexts is deemed too ambiguous to be used
for entity linking when not contextualised. In addition, the message language is
also considered, as names can be ambiguous within a given language context.
Evidence for a rumour is given by a message containing player and team
entities, and at least one transfer term. The talk will briefly outline the four
determinants of the veracity of a rumour: consensus (amount of evidence), re-
cency/constancy (evidence time decay), authority (evidence sources) and coher-
ence/consistency (evidence is not contradictory).
3 Football Whispers
In order to select tweets, which belong to the football domain, team names are
used to filter the messages, this results in between 1-2 million tweets per day, and
despite only English team names being used in the filter almost 56% of messages
are in other languages. The rumour detection system processes the messages
in real-time and the resultant rumours and likelihoods are validated by FW
experts before appearing on the website. The talk will present the evaluation
of the rumour detection and show how the use of knowledge graph (KG) data
has led to significantly increased player NEL performance, and identification
of the rumours concerning actual 2016 football transfers. Primarily this is due
to the availability of multilingual data in the KGs and the use of language
agnostic statistical disambiguation techniques in NEL. The success of FW (which
currently has two million users) has now led to the developed of Sports Whispers
and the application of this approach to other sporting domains.